<%BANNER%>

Memory Efficient Distributed Detection of Node Replication Attacks in Wireless Sensor Networks

Permanent Link: http://ufdc.ufl.edu/UFE0025072/00001

Material Information

Title: Memory Efficient Distributed Detection of Node Replication Attacks in Wireless Sensor Networks
Physical Description: 1 online resource (32 p.)
Language: english
Creator: Khanapure, Vishal
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: attacks, protocols, replication, security, sensors
Computer and Information Science and Engineering -- Dissertations, Academic -- UF
Genre: Computer Engineering thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Low cost availability of sensor nodes makes them an attractive choice for sensor networks and their applications. To keep the costs low, sensor nodes are generally unshielded. This unshielded nature of sensor-network nodes combined with their ease of deployment, makes them vulnerable because an adversary can capture these nodes, copy security information to make replicas and deploy the replicas in the network to render malicious attacks. Replication attacks can be extremely hazardous to a network if done in a strategic way. For any node replication detection protocol, the three most important design issues are memory usage, detection probability and energy consumption. Previous node replication detection schemes either incur large memory overhead or consume excessive energy, particularly in the central region of the network. This thesis presents a Memory Efficient Line-Selected Multicast (MELSeM) algorithm which uses efficient bloom filter data structure. We propose a novel distributed technique for detecting node replication attacks using MELSeM. MELSeM reduces the average memory overhead of the network by nearly 70% than the previous distributed schemes while achieving nearly same detection probability.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Vishal Khanapure.
Thesis: Thesis (M.S.)--University of Florida, 2009.
Local: Adviser: Chen, Shigang.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0025072:00001

Permanent Link: http://ufdc.ufl.edu/UFE0025072/00001

Material Information

Title: Memory Efficient Distributed Detection of Node Replication Attacks in Wireless Sensor Networks
Physical Description: 1 online resource (32 p.)
Language: english
Creator: Khanapure, Vishal
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: attacks, protocols, replication, security, sensors
Computer and Information Science and Engineering -- Dissertations, Academic -- UF
Genre: Computer Engineering thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Low cost availability of sensor nodes makes them an attractive choice for sensor networks and their applications. To keep the costs low, sensor nodes are generally unshielded. This unshielded nature of sensor-network nodes combined with their ease of deployment, makes them vulnerable because an adversary can capture these nodes, copy security information to make replicas and deploy the replicas in the network to render malicious attacks. Replication attacks can be extremely hazardous to a network if done in a strategic way. For any node replication detection protocol, the three most important design issues are memory usage, detection probability and energy consumption. Previous node replication detection schemes either incur large memory overhead or consume excessive energy, particularly in the central region of the network. This thesis presents a Memory Efficient Line-Selected Multicast (MELSeM) algorithm which uses efficient bloom filter data structure. We propose a novel distributed technique for detecting node replication attacks using MELSeM. MELSeM reduces the average memory overhead of the network by nearly 70% than the previous distributed schemes while achieving nearly same detection probability.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Vishal Khanapure.
Thesis: Thesis (M.S.)--University of Florida, 2009.
Local: Adviser: Chen, Shigang.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0025072:00001


This item has the following downloads:


Full Text
xml version 1.0 encoding UTF-8
REPORT xmlns http:www.fcla.edudlsmddaitss xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.fcla.edudlsmddaitssdaitssReport.xsd
INGEST IEID E20101221_AAAADL INGEST_TIME 2010-12-22T02:00:33Z PACKAGE UFE0025072_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC
FILES
FILE SIZE 27664 DFID F20101221_AABTNR ORIGIN DEPOSITOR PATH khanapure_v_Page_31.QC.jpg GLOBAL false PRESERVATION BIT MESSAGE_DIGEST ALGORITHM MD5
7c0ae16a938351967ae3cee13f9a5826
SHA-1
e0e4d35951276b156f487bd1c41e86c53e4dbe27
685 F20101221_AABTGE khanapure_v_Page_04.txt
f8b09a7027b72db270d4db17465585ff
b47f93fb2dc98a45c3a32a2b3c6ca44df4f1cf9c
85322 F20101221_AABTJA khanapure_v_Page_25.jpg
d497e239ee9a4f254f47899cbfcd345b
505a249756ad1b8a58272a1b4f008c1a9c96fecc
887 F20101221_AABTKV khanapure_v_Page_02.pro
5e4b7f813ffc722f676aa77498999ec6
f00fa4492209e8274ac891ca099922c2a1905b8d
6577 F20101221_AABTNS khanapure_v_Page_31thm.jpg
bfee176ce335a04f6705775e0f95e46d
8ee784400039856266ac3672510be650dc59110d
25271604 F20101221_AABTGF khanapure_v_Page_30.tif
c47b772bb4c9e2fb77776efbd445dcd4
ab4b990bccaff9f7cfc379a476538943bff244fc
39943 F20101221_AABTJB khanapure_v_Page_27.jpg
1acc8965a5bbbebe0e0b25bac6c31dd3
3da1f1a63d5515cc02da863ea6e37ae4e19b330b
35955 F20101221_AABTHZ khanapure_v_Page_04.jpg
9eb6e17924f3415621a9d166cfa47a26
f2213245ccbdf050540603fa854dc640669438e2
627 F20101221_AABTKW khanapure_v_Page_03.pro
1a4a289b1fd8ea5cf0d038c8eab4b718
9a009c2d7e118028a2b4d4725fb1e0504e77d460
12405 F20101221_AABTNT khanapure_v_Page_32.QC.jpg
ec6c128e1f64c2985121b0cd1a45ec01
fe1f62b62e21d932ec9ef899f84b5d598f17f0fe
4478 F20101221_AABTGG khanapure_v_Page_21thm.jpg
f1ff1aa35c6420da8366adc7a335266d
478156fab3222e70e5b36698a55a706512b5a66b
110090 F20101221_AABTJC khanapure_v_Page_28.jpg
95338dd74fdf2d29ddc44fd49b124ceb
c707e6a4dd164c0a869021676baaff2a95815b32
41342 F20101221_AABTKX khanapure_v_Page_07.pro
e0bd64b9299c35debef93ac872b27ae1
e5fe19208970e96f1e0803a4f4f1cfb9e7ecf25a
2728 F20101221_AABTNU khanapure_v_Page_32thm.jpg
3dcb3faff085813cf9e69db367a28ba6
2b8450e08c03672e4fc8473159809126248ec414
3949 F20101221_AABTGH khanapure_v_Page_24thm.jpg
97737238968d5edf70c1bc820096af77
bb806b7ab1745fa9af7c4ca583a08535bd805edd
34781 F20101221_AABTJD khanapure_v_Page_29.jpg
221c524dcd3fe7e83aec959dc76a66fe
df7f1e38ab6ca4e9b676db9ee3296f0315f98875
57694 F20101221_AABTKY khanapure_v_Page_08.pro
b9b15a81e9fc70b2310e1c6c50561059
3031fb49b34d426bc06a43cc5ad4fbd1ddb9320f
40881 F20101221_AABTNV UFE0025072_00001.mets FULL
231211ca6f5616dc4ef10b2bb995f62c
41d297ed9cf04f298d52ccde5d3161b34061ff3d
F20101221_AABTGI khanapure_v_Page_01.tif
f8c7eb4d68ee31d3fa71c7892966eb7d
015bb91426b0df13373a2582bd617e1d6475cb6a
127775 F20101221_AABTJE khanapure_v_Page_30.jpg
228fbd732318546e3008b6f984527588
db198199205e883804efcf4cc7b91fc07ff84565
2307 F20101221_AABTMA khanapure_v_Page_18.txt
88b5fbce16373989913b929fa857cc90
a2fd97aebf48e2b70c65917307d71d064e0f1210
64209 F20101221_AABTKZ khanapure_v_Page_09.pro
542f96d3641a9e39b7b8dce2d3cdf344
60cca85bf7aa936aaa72ee4427542fdff23c9b5d
100156 F20101221_AABTGJ khanapure_v_Page_31.jpg
f7256b85e092e412e3be507d084ebd2e
a6269eced16828641601526f4bfa2ea744627965
38929 F20101221_AABTJF khanapure_v_Page_32.jpg
0959a04ddbff880f958d1ec4f394d8ac
2bca9c9d894c9a6a5ce7e1a44008c5a175efb030
2284 F20101221_AABTMB khanapure_v_Page_19.txt
32d07ede3785c67343b4e50c2b81c16f
07654d3a7f2ad202394870f056a90570da46d016
F20101221_AABTGK khanapure_v_Page_11.tif
c338a83c96601228456fa2be5a547fe8
cb0684f6ff0f184c6ec134aa913e0429ac07efd1
27579 F20101221_AABTJG khanapure_v_Page_02.jp2
d5fe51473806853b4056e04a82323f37
ec5d18512a442c7c6b9d1220b8347b92acd150c9
1213 F20101221_AABTMC khanapure_v_Page_21.txt
91a9eb31173cf9a13849e00838d995ab
86178581ab7268a61edfb6312c7a69ada63f1545
F20101221_AABTGL khanapure_v_Page_32.tif
ab9c4194a559aa30a5a4c60fad5a4e25
f3e46233a1d3f601c9d53934fe72c30a3f911c03
18423 F20101221_AABTJH khanapure_v_Page_03.jp2
e70598f3f47239812372da25d01f9a6c
d2bf91491f50995f6ca9e1bb37587554480c1204
2177 F20101221_AABTMD khanapure_v_Page_22.txt
33813e1e510b24b0fb43d608d26590fa
d4fa5c5046879f1d62571d6b0cc527cd2cea4d1d
F20101221_AABTGM khanapure_v_Page_02.tif
bc2dc64f3e70e1d8e2d289896d68d143
e9d48d114a6152ee7d71653a323011f2b2effc91
371114 F20101221_AABTJI khanapure_v_Page_04.jp2
492870b6d58556238b2cc94c735f78a5
246993393e30b9f17d6f77cabc87b70c393b4f93
1820 F20101221_AABTME khanapure_v_Page_25.txt
919cdb278a41e9531943af21166dc0e6
870b15d59aa92c76271b65e69cef26e3302614be
8012 F20101221_AABTGN khanapure_v_Page_12thm.jpg
15f8ab670986739f574843895c49645a
150f9f9a7b08f319bccfd7562107bd3c6869660d
1051966 F20101221_AABTJJ khanapure_v_Page_05.jp2
96311f962335c03d92c064ca1d1f9464
dea811d990b2b0a1b36018f3d79de678dac4d87c
2155 F20101221_AABTMF khanapure_v_Page_26.txt
13aa0359b2bc1755cda13ef29b794115
070426f724e8f7dd06e6958a2856aa6c192a0904
11357 F20101221_AABTGO khanapure_v_Page_04.QC.jpg
1721f054dfb8bb103d92cbb614a3bfb1
8ee464000f6d4a8a196e186d1ce54a1214f9d5d8
702217 F20101221_AABTJK khanapure_v_Page_06.jp2
3fbeb532ea5c86997d804e6ce802a333
d5fe37d3a9101f0d167d179226576c97e1d8604d
783 F20101221_AABTMG khanapure_v_Page_27.txt
42650dc9b5c46141d57208167a403c59
b3d65b282f31960949bc0c0d3c5ddaaf4b4d043e
1545 F20101221_AABTGP khanapure_v_Page_16.txt
95697397625d9e90ef73b41074dd198f
c903403c4a2d163c3e5caa47e51a7fb563e9f938
1051938 F20101221_AABTJL khanapure_v_Page_09.jp2
a3b880dc60b56cc732dc95abe00d02aa
fb2d703af25d4d3d37a30ff3b0da78b765c59b62
2020 F20101221_AABTMH khanapure_v_Page_31.txt
572351bc1604c51dbf349bb1af395f15
12ef86ac0f30e8e19a8f0fcb85c79ad94e776489
49664 F20101221_AABTGQ khanapure_v_Page_31.pro
ce935bd18164ad6cdb1ea8efbbdf904f
e7de503f2657b07e1de46a8741637e6d39a88c14
1051984 F20101221_AABTJM khanapure_v_Page_11.jp2
a5fc84572d9c0cf319b6df273ab615fc
d07a39f5f7d7274723003df47cdfe1c3f8e649ac
711 F20101221_AABTMI khanapure_v_Page_32.txt
daa84178abb6744c3670c5f5a6573c20
52301d65e08647c1d46b30c0ede0b1c7810024ab
25916 F20101221_AABTGR khanapure_v_Page_07.QC.jpg
841eac71bcf2972cbd973b669f923593
7534b9a59dace3a688a6d86a4c4e921ea8247975
1051981 F20101221_AABTJN khanapure_v_Page_12.jp2
8b091b2f30ff127622d8214b62433649
9f36c6830ee08dd5bc0e419c16b0cb3f3b7bc568
312781 F20101221_AABTMJ khanapure_v.pdf
41428a2efeb3c15615bc2755add10622
bbb37a5afe047d1697061bdbc9a4cd42c66d3df2
84 F20101221_AABTGS khanapure_v_Page_03.txt
fba33857be850e80f7aebc8b8e5575f6
5abd402609a604f60203ba06da914b07b33cfbe7
1051977 F20101221_AABTJO khanapure_v_Page_13.jp2
5a5bbe0bf4765497df7bdc98dee790b9
970819a8e6bc6c5cddf27b65380fae183e4eaf20
1416 F20101221_AABTMK khanapure_v_Page_02.QC.jpg
b2b8d4c3be86826320c1c90bc1f4f40c
07f192d7060dc4cd2880f3f5273240d85a32d731
8885 F20101221_AABTGT khanapure_v_Page_14thm.jpg
c06ad411e27e1fb86a20166ef3e5b95e
8e5410b05fed3adebe6f10ed99a9add11e8db2d6
1051940 F20101221_AABTJP khanapure_v_Page_15.jp2
56a907b2fdbb345cce1e204e4a32954f
2cd38bd7ca3160358c3a7b3f0769d93f661d3f06
481 F20101221_AABTML khanapure_v_Page_02thm.jpg
6039143de30e1edb44ccbd1a64441f13
50fae4b074f3b9780b7f611929e8878b498774cc
631 F20101221_AABTGU khanapure_v_Page_24.txt
3b412c0dfac5ed42c12a38ee21c9e289
c257e678e35d52c4e8164e8e8d663983b1cb5f06
862840 F20101221_AABTJQ khanapure_v_Page_16.jp2
7c558d4879f933070c0eedb9200214af
571c4aefa0ce53426cab6422f09baeae5f710b66
441 F20101221_AABTMM khanapure_v_Page_03thm.jpg
45a9e5085714588ec52490a06ce6b780
04d5a60a8ff7ffae615ab9d3203d45541dab10f3
13985 F20101221_AABTGV khanapure_v_Page_27.QC.jpg
b928d591e2f94b94a0ee3cc07bd09cfa
21deccb3d67fcf22e52b68bb85b801265c46dc8b
1051983 F20101221_AABTJR khanapure_v_Page_18.jp2
7337e99773109648bf22c7710e5bf324
d9768f4522f9f400908d498ce6c3810816a57711
2623 F20101221_AABTMN khanapure_v_Page_04thm.jpg
0f1edce2bf55b059826501a94ce63cf5
e23e20594a6a897bd8986d6065dd41a4e33f4c68
34297 F20101221_AABTFB khanapure_v_Page_19.QC.jpg
4a64311f11b2ba21eb046614cc4d0b40
fe2be4b8ad534a99310378135180615a0ec22e76
34333 F20101221_AABTGW khanapure_v_Page_18.QC.jpg
c0f084884eb9b657d5ffd8d63d8a3d49
e737474f9d20802f026fd1340b2485af2e55de8c
F20101221_AABTJS khanapure_v_Page_19.jp2
a4b92603bcb921875ca573f64d9eb211
55d035555b4774f08af2753fdb86beed6de98a43
3223 F20101221_AABTMO khanapure_v_Page_06thm.jpg
1a44316bfc670e312d134e10f27df444
d29e67b542111f6ed45e65056049dcd6b421b3b8
1051974 F20101221_AABTFC khanapure_v_Page_08.jp2
05b886eff61a5b8d63a01885a9ee4d5d
84dd27ad2538aa09fbee1bff0dee655c76af5d66
8215 F20101221_AABTGX khanapure_v_Page_08thm.jpg
b10395fc4390c0091bafe8fd022f8ef9
f83870b102128721b9ef47cd7f53f562dcba3f2a
F20101221_AABTJT khanapure_v_Page_20.jp2
cfdc5d2eefc74742459693edfbe3de28
56c69517167c687b31096b2eb307dfe68a0f5cb1
6071 F20101221_AABTMP khanapure_v_Page_07thm.jpg
832e921db7c0b8d956d4974663a2eeee
6ab9470a3dc19670e2a5d24b68a7e643e56ddcca
15591 F20101221_AABTFD khanapure_v_Page_06.pro
a7d4a390816931086817709234939266
cb6ea84eb8c50dd7763c719f8d292ab2b6b78b0a
622226 F20101221_AABTJU khanapure_v_Page_21.jp2
5f2bf9a184f4553b8ba31d764bb7513c
ba6f10f2f15b40afb32eb157a2339474d8140263
8861 F20101221_AABTMQ khanapure_v_Page_09thm.jpg
8b73c06f0b6ab0516f57247dd65ffa5a
c36e5b6b004ebffa1fd0e38c5ebb9c4ec0121ae6
27781 F20101221_AABTFE khanapure_v_Page_05.QC.jpg
62faa6b0126d904c9d6320911397273c
8caca81964b20c6c9869501031141187e0a46a60
8104 F20101221_AABTIA khanapure_v_Page_28thm.jpg
2db747ab9a2029880c9c6802ae0f7c29
200bce1f15bd2e59b18d480810a003a69bb8c6ef
1051972 F20101221_AABTGY khanapure_v_Page_14.jp2
489e83f94ef850af6312a63749f6ae98
1ab229a9955ade5bd18e14415e532890a7cdf391
1051975 F20101221_AABTJV khanapure_v_Page_22.jp2
c0c6de04eaff2de436156497c1d7e23b
50355281707143f16a0e02ec2b39825c0b1022ca
22770 F20101221_AABTMR khanapure_v_Page_10.QC.jpg
6e21bdb0e2aedb53ae86b6c3590d3dac
aa9617cfd52652cd1827b915210e7b11f772ee26
1051945 F20101221_AABTIB khanapure_v_Page_17.jp2
f12df93106dd21f58aaab91ac2b16b01
9e097806d11742f32835693d573674692fe8df43
39152 F20101221_AABTGZ khanapure_v_Page_09.QC.jpg
39a28a6e38b10e28f8951a0464b41b76
2ae766f2a38eaced98ed8b1972d843afafa35f61
922805 F20101221_AABTJW khanapure_v_Page_23.jp2
cac779840bb906c74a2fec59e657489d
bccee5cba514a49322cba8426093925725d1ad59
5192 F20101221_AABTMS khanapure_v_Page_10thm.jpg
ccfa9197f1feb54c0e2f700fcc13375c
935ae84336a35ba906a3dc82d31d5fd72a695f58
107868 F20101221_AABTFF khanapure_v_Page_22.jpg
1b992ed347c54e269a62c863b8dbb3ce
97327f7f8abbc88a8cafe0fd5d7ea0707a539c29
1964 F20101221_AABTIC khanapure_v_Page_01thm.jpg
9dd87daa1c222b04a7c8ded770733b08
c39b1ee681acb4e75f0b03169522b27f0d627e91
943884 F20101221_AABTJX khanapure_v_Page_25.jp2
a024c30a0b56363e38d7f59f97f8e633
114ad7f459bbf8e1c169c3c7b4ec22df53f72b22
32314 F20101221_AABTMT khanapure_v_Page_11.QC.jpg
ac7d6aa7dcf4767e8232c4b33ba7f3e5
65adac278c501e2a94c29431fe3a6ee773bfa3ea
31480 F20101221_AABTFG khanapure_v_Page_17.QC.jpg
99db806db3e39a29d6ef164ad0c24d25
68eb702913b61be0c40ff809d3d119ddcc0f7ddb
392756 F20101221_AABTJY khanapure_v_Page_27.jp2
6c196e6f96c9fbde7a83f64c76e393b2
a6b5f83d5034b5373afd19b3b657e51840c44c92
33403 F20101221_AABTMU khanapure_v_Page_12.QC.jpg
0e4371b924d5a3728d75ffa2eff4ba08
2ab8599f8a3c02d0b8169ab1876a5095e07d152e
113076 F20101221_AABTFH khanapure_v_Page_18.jpg
f0f58de9eebafbe0bd352ec2d9c67f90
f54a8d79739f43682a6299b9bc9266a10de361ce
34224 F20101221_AABTID khanapure_v_Page_28.QC.jpg
c7a1009a47574d1c5d777526807a986b
9e1071561197cc6597f9cc2192e7fa94b9734d70
35006 F20101221_AABTLA khanapure_v_Page_10.pro
91265b809eb282c6dee604600377c827
cb287ea45364b4972b394be3157aa7fd10437735
1051980 F20101221_AABTJZ khanapure_v_Page_28.jp2
276eb287e951d1e608c4974933974d2a
f0468a2f2643a0a1f4f78fd035cc9e7c13f48e61
33593 F20101221_AABTMV khanapure_v_Page_13.QC.jpg
50465a4aa1b5532cbe99c5d89159e1e4
f5384ff79fc00f42f541ef7651e0ba9784b9d4b9
34393 F20101221_AABTFI khanapure_v_Page_22.QC.jpg
561268b1ad396aae853fe9bd6126f865
43ebcf9773024c82f4753b93a33cec3ff3eb7951
1428 F20101221_AABTIE khanapure_v_Page_23.txt
a2ee4181d0cca537f3638fb9fd6841a1
49c70692c94cc87ea1f039d06b3dbbc025241e66
8122 F20101221_AABTMW khanapure_v_Page_13thm.jpg
a285a1ca82415de2b5e07b7829ef9954
a859236090332bf16cc4b32838f2ab0116dc734a
1051933 F20101221_AABTFJ khanapure_v_Page_26.jp2
cb2f322f8eaa48696e0517d4a1339728
4553c0d74e433356a069a1a404044967d9f86020
2154 F20101221_AABTIF khanapure_v_Page_20.txt
b68378d16aa0f325978868d7d6022cc2
015c5c70d3ab691f872c2883ad4124cd1a31979b
50707 F20101221_AABTLB khanapure_v_Page_11.pro
090b821dc221615b1984478e2c16a3bb
d38f517c492f27522c4a3dea0a02ce3b27fe9169
39415 F20101221_AABTMX khanapure_v_Page_14.QC.jpg
0fb8c7b42acbc60a65876dddbc8e380f
826bedfb4ae4e9ee11bebfa1f191558432f3d9e7
350495 F20101221_AABTFK khanapure_v_Page_24.jp2
5a4b1b7aaad385f236467101a26d37f0
10bd04f8ece839ae7e9deccdde07c7460ba658bf
54371 F20101221_AABTIG khanapure_v_Page_13.pro
872d6ed1cce88c7942fc47593bc83d08
2d4eb06fa69b545e739a9f688701fe25a4c48411
52636 F20101221_AABTLC khanapure_v_Page_12.pro
1c11da1b20e93b5b2b3aaa9669a55439
b47f1e34bb4b95b6cf5275b589a02bae24facd6c
37532 F20101221_AABTMY khanapure_v_Page_15.QC.jpg
f46b2517051fe30b2dd34913a5f9571e
363c964d21584d21bf8a3e7d5d66ba49b67ecc75
F20101221_AABTFL khanapure_v_Page_12.tif
47d8008b1bc4d843dff4642b1674a890
fcd74f74478274f3b19353a038b194bbb70684fb
30579 F20101221_AABTIH khanapure_v_Page_20.QC.jpg
bfe1c15f23d41963e283f079cd607587
54579739f2c7a58d03e0fdde49433bd6d814b46b
60857 F20101221_AABTLD khanapure_v_Page_15.pro
16fa7f3d2d06941545156b825620d99b
84b0b25a29be0acd815e0b6f4582fb3fa4ca55ab
7623 F20101221_AABTFM khanapure_v_Page_11thm.jpg
9feb736365621064ab0ce311a05a47ad
0ccf38c46326f5e85f63b0f72715b86a8096fb32
2534 F20101221_AABTII khanapure_v_Page_30.txt
d12c9826bb6fff1e73cec49d83c46f1a
17b7e1cebf0db004568b10697117d072568009d6
38869 F20101221_AABTLE khanapure_v_Page_16.pro
52ecade13f0d87e7cd66660e215d90dd
80cb565c808fe5d5d6db016396393abd8755addf
8587 F20101221_AABTMZ khanapure_v_Page_15thm.jpg
be79badf8b4e9e6569c8640cd98ea34e
e577e04ee1f7bfabc0fbcef4feb4c6bfbb65f5c9
92615 F20101221_AABTFN khanapure_v_Page_05.jpg
61a5cadecfda4ddfd32256fb88761b79
d0482e6e4cc385511b394269cd107ed291f0c6db
F20101221_AABTIJ khanapure_v_Page_04.tif
0836853fab4f5b91bf01b3e91a1df87b
5a568a9010d3dd0769e13115870f34c5a392200e
51039 F20101221_AABTLF khanapure_v_Page_17.pro
ee2ce7e9d0b0b88018a223f4c7b9abe1
7df2fa7449dff81adac9094fabed5489dd817e03
958 F20101221_AABTFO khanapure_v_Page_03.QC.jpg
c6e3476e304a27e8ebe676c96c9d4a22
60e5fa971043e54aa15c571c2b725e0df799aab7
52522 F20101221_AABTIK UFE0025072_00001.xml
c4f1d2dc29cad00cb9316cbbdb40c4ba
da30b98bdb21d2d3077e31593b5793de3df1d2c6
54488 F20101221_AABTLG khanapure_v_Page_18.pro
4b822bd8b773b45cfb6271065cb4eff4
4f7079f914a045cc75874b9c89ab1a0d14f17b50
1051982 F20101221_AABTFP khanapure_v_Page_30.jp2
5e6130ba69e554fbee14ef1c24cbfa44
a1140211dbd39f69fab2153ece942401b6b959a8
48459 F20101221_AABTLH khanapure_v_Page_20.pro
1bb6f5fc18d5d3a5f381212649b03045
2ffba083f9edd1a0b4057f5d51c69a60fa9bd2f5
17060 F20101221_AABTFQ khanapure_v_Page_32.pro
6cd9652c868102f50efb977a45162074
03bcb6ff0f35bb93e31675b344ffed5fa4e9502d
35566 F20101221_AABTLI khanapure_v_Page_23.pro
b6599334456043535e230dcf0da718c4
3c519968705b20b4b9ceb4143c19eeddf6365664
15292 F20101221_AABTFR khanapure_v_Page_29.pro
84bbe46792d08ad62c8d24d482d23fff
a524a9813e247aefe61f97559d22eb2df10491db
45087 F20101221_AABTIN khanapure_v_Page_06.jpg
8ea4a8b4b51059cd4582f3f6daac6b2d
81904ebdec7a5c29912c6b8022a1c89df2700439
11075 F20101221_AABTLJ khanapure_v_Page_24.pro
a688c3ff19496edb84fa055cf2464db5
1e9badfa54a2c6df3d0f7bdc701f5755e445766d
961136 F20101221_AABTFS khanapure_v_Page_07.jp2
99363fbc7f3cf484db231211b05e09d0
3fd45463c9a4be5104e61f0c66c8dfd60b595a01
85854 F20101221_AABTIO khanapure_v_Page_07.jpg
0f3d89e942116659e109184844039254
85a12571381765098cfacca4cf6ac9edf8b09687
37813 F20101221_AABTLK khanapure_v_Page_25.pro
984a084ec2d4785650a033cf36e678de
40f97279879108d4677b07bcd2bfa4229a872ab3
27580 F20101221_AABTFT khanapure_v_Page_01.jpg
8f8739c418b434c87ef0da9857d6fc54
7d587a7239720517cfed2ba4890eb3bfba9c3792
124413 F20101221_AABTIP khanapure_v_Page_09.jpg
57ed493584b95d6ca52dcfb9c0ba976d
1ac580fa9ba3340931e667a4a8d84a655ba70b72
10120 F20101221_AABTLL khanapure_v_Page_27.pro
73f081c7e1346781fb2f687629002291
6d8cb63934fc6c942d87268d27437242097c980b
109873 F20101221_AABTFU khanapure_v_Page_26.jpg
fbd0c3b46fa5f0df563e226a318c4787
72a340812f7412ed3280a50a2c392bf657b20d4d
72773 F20101221_AABTIQ khanapure_v_Page_10.jpg
658f05c046b0638f06d55ffedc933c44
42aae13edd9705ec2ef3ab91cbdf17b2ecc7849b
54522 F20101221_AABTLM khanapure_v_Page_28.pro
9478a72c17a7258ae1ff1757e6433bba
342249bbb5df11b4a27d24a3cb4377cbec649d68
F20101221_AABTFV khanapure_v_Page_23.tif
6a4aa9670564e0c7a33f3b4458bfec03
b73c753cbc18f9949ba43a73b42022827e141a4a
103660 F20101221_AABTIR khanapure_v_Page_11.jpg
97ad859b92f875a85e8f6d65644440b4
fb8ad29aba1e55f4ce21d3788c5c6ef359f3d21c
63221 F20101221_AABTLN khanapure_v_Page_30.pro
55d5b7be2bc001a849c3cff4d1e8dff8
7c09f983ee2ae7bf0011a0cd3431cf0aec9a9c14
34804 F20101221_AABTFW khanapure_v_Page_08.QC.jpg
033102469418c2dfeea524c62cd2dba0
33da9c511be15342770c2a66a86159f86f2b3f6f
104718 F20101221_AABTIS khanapure_v_Page_13.jpg
80ddef197de600b3dc6e470d5149a097
7b7b867d5a70f08ca2767c5f70ad449eed716dfd
82 F20101221_AABTLO khanapure_v_Page_02.txt
77ccbfcfbbde20dab658f75ec2c6b026
96c521e3f85e40fd78cd9fa9ba9607186dd6f51a
119721 F20101221_AABTIT khanapure_v_Page_15.jpg
01b10f55f4d580c808b21d4273a51d7d
f68951e223ab3dc648902e1543bec219f0fd2485
2199 F20101221_AABTLP khanapure_v_Page_05.txt
4bd82f45006539e1f8c1ebd5f92f9ab6
f9a02260eca2bd52cd7da709eb7b11a8c2209547
64299 F20101221_AABTFX khanapure_v_Page_14.pro
fcaa1048300e9b1b705d00cbc118f5d4
0fbab062f487bb7e02211043f5b9b438ada29607
78935 F20101221_AABTIU khanapure_v_Page_16.jpg
269679fa34484f0e3e5cf28bd37f172c
7ca525bfc5e91446988486fdd4f2949ea264d7d2
1832 F20101221_AABTLQ khanapure_v_Page_07.txt
4f9549027113a292c5c6c26d23eea601
d8b1f3714393976275655eb3e0976b6ea77fc238
8522 F20101221_AABTHA khanapure_v_Page_01.QC.jpg
59bc52dc5b11519fbf5a96a241100694
61069304402eab3f476eee72e75c566ecf4a91e5
52716 F20101221_AABTFY khanapure_v_Page_22.pro
6ca5468a4f3c96f77407ba6fb4cc3eb2
429dd48d1c4d31224225850db409a0f12500b763
99731 F20101221_AABTIV khanapure_v_Page_17.jpg
6ffb8ef1ae9a65a77f8cd427097b138a
bfa255f999df9b247cfe758924dd5cef108d26d7
2338 F20101221_AABTLR khanapure_v_Page_08.txt
429de8b4527cf7c55a2fde32a943c18a
478c2869d4cce17f6509b6faa3a71c992e462bc5
F20101221_AABTHB khanapure_v_Page_27.tif
2e93e25a12d29143e458893a5e971f7e
6a30a81a0a33af009b36781b427e79cc7d08ef2e
16167 F20101221_AABTFZ khanapure_v_Page_04.pro
792c6d0e9e201eeecf8365f8e62f4aa1
ec1a2f037da6952c9e21927c1fde3eafcc1ffcbf
98625 F20101221_AABTIW khanapure_v_Page_20.jpg
31df6f877dafef562615eccaba52b227
3addb35ab2ad518f111d607c5a2f2ceeda2f645e
2510 F20101221_AABTLS khanapure_v_Page_09.txt
28c3daa37032ed19f7ceac5e2c031279
92f145ef79119e6deffe22ac9d200cd759dd9483
13578 F20101221_AABTHC khanapure_v_Page_06.QC.jpg
1a63a5faeb1da2fe6e2ce40ea1d11b91
f2ab8814d5e13853f73e0c3f7328c5e4ca07931e
59503 F20101221_AABTIX khanapure_v_Page_21.jpg
d060470870cf346ef88f963da3f42c13
bea9e601a826515a09626c1ff2cae7179a6b41d2
1395 F20101221_AABTLT khanapure_v_Page_10.txt
0403fa2ae2d7830602cb0752483953c2
9ed4a28ee586d45cbfbcb8d475fde76f40d58fec
261422 F20101221_AABTHD khanapure_v_Page_01.jp2
c296d20fd9430f75ba5f4cd1da305015
d013b1b2b09be8046ccaa66783903a3ee86ced7f
90069 F20101221_AABTIY khanapure_v_Page_23.jpg
e72754ba277853f41d5e3e6a2a07a0a0
9e458095c270000887dd023c7a740954dc2bf5c7
2093 F20101221_AABTLU khanapure_v_Page_11.txt
d132d7ea85919df46b3afba1f6cee676
4bc5f7aeb0fa04249b9124001d5084933f7ef5bf
F20101221_AABTHE khanapure_v_Page_05.tif
bb071a80ef6aec333aed5e5f6fa2c6e8
2ed9cab12a53e3c2ae472d205c4ec8f7731933a8
405162 F20101221_AABTKA khanapure_v_Page_32.jp2
3417144d4a36cf90515a86d28f2d57f9
f569ebf1149a473b6fb1c04f72c98b6b7dbfee23
36126 F20101221_AABTIZ khanapure_v_Page_24.jpg
af3e528aab7895e95024b2ecbd2ee601
aa8856ac7ea5dbd6e8b995620f93bfa7c1f4816f
2114 F20101221_AABTLV khanapure_v_Page_12.txt
67b76a86960b632d0aad559e0af6e83c
73432db8609d4e49128949e6e80138e5cdb4c0d7
649 F20101221_AABTHF khanapure_v_Page_29.txt
720165ac68f0c24f71a6e4c6705648ce
f0ff7b8efaf9da27a851de0118d9167c51a7f4e8
F20101221_AABTKB khanapure_v_Page_03.tif
92505a0fe17280906c4c19f821c229d1
52bb08fb790167467c7bc45632470c2d82686a31
2170 F20101221_AABTLW khanapure_v_Page_13.txt
065c4ecc44767275b5ee1e76c968f68d
41ca5939fff98250d7edfeed9ebae775fdf46798
647 F20101221_AABTHG khanapure_v_Page_06.txt
4447bcac7877aaaf7f48342f96153d8a
779f1a9b2ff8134d0911f331b4b11d86f08fa48c
F20101221_AABTKC khanapure_v_Page_06.tif
8bf0698e3cc05b0551628cc3cdec8bb3
7c7d36ca4a6a6a9bb464c84d6eba783f051ca6e6
2513 F20101221_AABTLX khanapure_v_Page_14.txt
a3e0094903e8de858ca18c72b3022b53
50978489e2127916cd8742456341b4f540478825
4090 F20101221_AABTHH khanapure_v_Page_02.jpg
6c3765fd540eb8777b4ed024f74247c1
ead45ab24adc6ad0c477cdb54a7b6652dd7c188d
F20101221_AABTKD khanapure_v_Page_07.tif
be7074e38f72c87ccee98cf0490d2039
30dd637188d04806fd6b1feee89503ab4adee807
2386 F20101221_AABTLY khanapure_v_Page_15.txt
59da6dcb7c11c51d6f518516cf46dedd
b784828b285a2e6e7e062194326491c2d3fa6149
353317 F20101221_AABTHI khanapure_v_Page_29.jp2
3d3de116710cb157bf19e05e886075d3
6598063e7853f3d4d391953557ae446f2c41f41d
F20101221_AABTKE khanapure_v_Page_08.tif
b49e1d691a721d321782727a03a79980
2ef6a80f65f3b4882136415fab18d4803c2b8acd
24813 F20101221_AABTNA khanapure_v_Page_16.QC.jpg
14261b2c230aa1ada19b63fbd2d35c51
e68eda859014da4bac7aa98860456df9a823ada1
2146 F20101221_AABTLZ khanapure_v_Page_17.txt
771043d868ee910e96c97b5cb1eb0119
ae03f1a30ba31cc823ac69015ab7ec4db61d9f11
50957 F20101221_AABTHJ khanapure_v_Page_05.pro
2f7bcf974b3c3b3bc331389d5c6d634b
fb816cc8fbbd6000ef5ff8e2d4ca73d6923b739c
F20101221_AABTKF khanapure_v_Page_09.tif
f10f7f7d3aac515f16b18e8262b3af53
129a391c1502e2f56900af8c74b7ebcaae2af6a8
6026 F20101221_AABTNB khanapure_v_Page_16thm.jpg
ca3dae11440f2a8063edf830c036b1b3
0265ecf164aaeb8a2ea3604f439272f6543d3e18
12408 F20101221_AABTHK khanapure_v_Page_24.QC.jpg
f5a44a2c4c5c6f526b40943b444e815b
d2314cb10c1d54ba0774d3218e65d63b70e7e6bf
F20101221_AABTKG khanapure_v_Page_10.tif
da2c728b60143a6f45d5068efd2350dc
2e68d339f514ef85e411a409adfd4a49e82dd627
7914 F20101221_AABTNC khanapure_v_Page_17thm.jpg
914f07d65ec930ee2672556d1deaa1ee
8ba7cd24c01d5ee4f5d7879dffa6988bfc3595c2
8409 F20101221_AABTHL khanapure_v_Page_01.pro
df343db6b8dcc973fd279ef7bb72e5c3
cc922367cf311b6eb19ea11861379fe60ff299dd
F20101221_AABTKH khanapure_v_Page_13.tif
bb4163596d94aa369fed9c5e9627dffb
6659d2b4de34071e8f25be6cd749eefa430fdbbd
8013 F20101221_AABTND khanapure_v_Page_18thm.jpg
e76133b517224ed8cbbcff3f1e306507
b323416f2f7c0542e43509222ec966e7baa44a37
53053 F20101221_AABTHM khanapure_v_Page_26.pro
2d48a1869d3f0fddc086c740410b3493
097022e6b523db9cd4ebc03794b8c7874c5c727a
F20101221_AABTKI khanapure_v_Page_15.tif
34401272950483c59d1d68ef93eccfce
b602f33062b0632010874781a32b3d0793d30dc3
7934 F20101221_AABTNE khanapure_v_Page_19thm.jpg
f0e2bc1aea864360578d397c6dc91f96
6f2755d4434cf2209ff2addab569f8758942ba7a
2256 F20101221_AABTHN khanapure_v_Page_28.txt
834d42e9b06be993930c4d2dc0f26180
641baebaa9523b7fc2a9717e07b1b59a1c656ec0
F20101221_AABTKJ khanapure_v_Page_16.tif
6070cc2e30e9d8ff19a2a7ac07c87c17
cd8a76310c254e3b0b828d2f4334f2576fca058e
7425 F20101221_AABTNF khanapure_v_Page_20thm.jpg
d552ed63aa729b8d9a8677431bccc0f9
e6ae6d1414494e0b010b05245080bcfedc97787a
5734 F20101221_AABTHO khanapure_v_Page_05thm.jpg
6d0bbec82eb2b74dc92f78209fa233c6
9886e3db09f9a082c98127fc4d8181a8c6e62b85
F20101221_AABTKK khanapure_v_Page_17.tif
574827e6dd122de300dce19144f1f6c8
2c90d80ff2ef8f82ef99a6f94211153202371ad5
18875 F20101221_AABTNG khanapure_v_Page_21.QC.jpg
00ffe5214de7c498d7a4a29962f3a35b
8d5b7cd699c62287622e25873b84b2bb3227dc32
125286 F20101221_AABTHP khanapure_v_Page_14.jpg
fd1117f11743f4ca64b3ad29cd056259
a7abe83c0152f0928a949ef62da05108d1d2b277
F20101221_AABTKL khanapure_v_Page_18.tif
9151cf73eef0874adff6bb4535f91004
f6ce83ef80751368770a9374db5e87f6d9bc4abb
8173 F20101221_AABTNH khanapure_v_Page_22thm.jpg
50bcc63127aade343551829493bddddb
d115f511b9b72deee29d47682e7de56596fbf02f
F20101221_AABTKM khanapure_v_Page_19.tif
2f96cfa959d60a054866fe48b8801272
dc204ca90fb2fd26d57497aa1537793e364d0ff4
6575 F20101221_AABTNI khanapure_v_Page_23thm.jpg
4fb31c7e15882a2ea934dc959f3579d2
bee432e73e3b2b6e36bc1f43222bda74dd42043f
28851 F20101221_AABTHQ khanapure_v_Page_21.pro
c845c8387fdedf301f299862d028b6e0
3c2e053a64c5ccf8f6ac06865fc965be44f665bc
F20101221_AABTKN khanapure_v_Page_20.tif
12e07e32fe473046c69b2fc850090471
0aeef5103c052d0e5a0b9644b81f0a6150809dae
27467 F20101221_AABTNJ khanapure_v_Page_25.QC.jpg
ddccc72e58717772fb4954efd5636169
2feeb4efe20ee1e935c453acdfec04d1d5a4044b
102409 F20101221_AABTHR khanapure_v_Page_12.jpg
d9621b9034732a4037e705bdcf8d501c
93dce9365c4fbb824063b04d0f1d0cc556b1889b
6514 F20101221_AABTNK khanapure_v_Page_25thm.jpg
723c3e07d63681effd444ff55232d1c9
6fc4c0283771952df8597d8755f8cf1703d1bc92
34243 F20101221_AABTHS khanapure_v_Page_26.QC.jpg
1318caaece0f50755ecbaa0bd3ae5b4e
1cd3bb2b286c061eec72c8f3dbca69cd39fe65c4
F20101221_AABTKO khanapure_v_Page_21.tif
ef769f02f0f4284607659d229313b6de
729a73a088ab129df5e6ac8a0fec5e5f3fa1d8ad
7680 F20101221_AABTNL khanapure_v_Page_26thm.jpg
ceb50690a53045f61f668c539bb737ac
d6d770664d3bd36b5fe133d5fe96a6f68355946f
482 F20101221_AABTHT khanapure_v_Page_01.txt
4e695c69c99d6b8d6ed58dfc75db8b80
a467d544504704899b969c2fee350310fb037793
F20101221_AABTKP khanapure_v_Page_22.tif
c3cb59490eebb87ed7ffa046cec83030
ce75c2c80db7d0466af7a85f7e8fc07b7dbc51ba
108127 F20101221_AABTHU khanapure_v_Page_19.jpg
6cbaa1d722ceccf9ae622fc062bdb011
f506889afefb3870cd615e98e017ecdfaeb547fc
F20101221_AABTKQ khanapure_v_Page_24.tif
8795d24fb2a026ca27c184a6edb8b536
03ac64e4694d7fb43320fac55eb816838cd8a6b8
4388 F20101221_AABTNM khanapure_v_Page_27thm.jpg
4f3e8056712e97289446b7101c92d116
67989e7972f779d33b0673bde437ae055fe4c46b
3288 F20101221_AABTGA khanapure_v_Page_03.jpg
4700159bc4c32ece57e908219deb4f45
560a74f1910280dfac39edb1490b8076fe38a197
27653 F20101221_AABTHV khanapure_v_Page_23.QC.jpg
4f74b099c66cefbf3a857ac58cc2c0ee
e813cdc3e0d1b107b9bd5e6e3ed180351c1970f0
F20101221_AABTKR khanapure_v_Page_25.tif
a7b27112f132d77225323e8a8f0d6a3f
001047d7181bc8410ae28bd07b846bd1899e41b1
10853 F20101221_AABTNN khanapure_v_Page_29.QC.jpg
a0013c4d7e6ec536e44da6b82dfff859
2655d20778ea0cbcb6d45bd9a0e8c44acc59a9ac
57197 F20101221_AABTGB khanapure_v_Page_19.pro
86c42d58359832ce16998bf7b8e5b519
401f31086c82ea551bc440aa2cecb4c04149e2de
113006 F20101221_AABTHW khanapure_v_Page_08.jpg
f41c75f37d0d2400dee21e5c7115f55c
832ba6010be6d0d076dd41b45744ccb277c999ec
F20101221_AABTKS khanapure_v_Page_26.tif
45e33502aa36d2f07b54e30c170b5668
059684e52cddca81a79f2ca549e3f90fcd2d5c5f
2458 F20101221_AABTNO khanapure_v_Page_29thm.jpg
c31e6eb4eb9356669b3e82fa9050f490
4f385696141b5b694c6f17da38ef2deda98b11f6
35186 F20101221_AABTNP khanapure_v_Page_30.QC.jpg
facc1c0c95f3f28b94eab181bc80e4d4
dc3fcad71e47f2e250982b73c88fd1b4c6466d16
F20101221_AABTGC khanapure_v_Page_14.tif
184922238643a6beb27749869d64f017
6537b9175b26f7ac6d6ec0c13cf5bf3092e33b98
796032 F20101221_AABTHX khanapure_v_Page_10.jp2
fd68ba324c935dc873071d0ab73bcf81
51bf910f8e67bffdb3fc982362cf9943f3189709
F20101221_AABTKT khanapure_v_Page_28.tif
88626727b4e6ecd8f044dfb0a65d31ae
db807ca56adc2a9fe15f384daa8a6b82f8a9d32f
8608 F20101221_AABTNQ khanapure_v_Page_30thm.jpg
0de066cb51973bec28cbd45354b2638c
c333e80962ffd1a23f81e678be38613ac0b8b409
F20101221_AABTGD khanapure_v_Page_31.tif
f05a683a3e3eebf5f68d7c75643af33d
77834f1b9d4cce3d49069bf7f30144b0655420e7
1051957 F20101221_AABTHY khanapure_v_Page_31.jp2
c4f1c1331f73907ecf781efe26e2dc8a
03c35c575e39eba80f2bcb6b0a120c9567eb2cd3
F20101221_AABTKU khanapure_v_Page_29.tif
9b03fb8cf0e42e648d3ed7c6d268ce46
13f22cf5d9a4db04121d8b49455498b1531e44fe







MEMORY EFFICIENT DISTRIBUTED DETECTION OF NODE REPLICATION
ATTACKS IN WIRELESS SENSOR NETWORKS



















By

VISHAL KHANAPURE


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2009






























) 2009 Vishal Khanapure




































To my parents









ACKNOWLEDGMENTS

First and foremost, I thank Dr. C'I, i for his invaluable guidance. Without his

encouragement this thesis would not have been possible. My supervisory committee

members Dr. Prabhat Mishra and Dr. Alin Dobra provided effective positive comments

and -ii-:.- -1 i ..i for which I am grateful to them.

I am fortunate for having extremely loving and caring parents. They have been

a steadfast support all through my education and have constantly been a source of

motivation. I am also thankful to my friends. Especially Aparna Venkatesan, Rebecca

David and Ming Zhang for their -ii--, -I i.. -.









TABLE OF CONTENTS


page

ACKNOW LEDGMENTS ................................. 4

LIST OF FIGURES .................................... 6

A B ST R A C T . . . . . . . . . . 7

CHAPTER

1 INTRODUCTION .................................. 8

2 BACKGROUND AND RELATED WORK ........ ............. 11

2.1 B background . . . . . . . . 11
2.1.1 Sensor Networks ........ ........... ....... 11
2.1.2 Node Replication Attacks ........ ........... ... 11
2.2 Models .............. .. .......................... 12
2.2.1 Network Model .. .... ............ ....... 12
2.2.2 Adversary M odel .................. ......... 12
2.3 Related W ork .................. ............... 13
2.3.1 Centralized Solution .................. ........ 13
2.3.2 Localized Solution .................. ......... 13
2.3.3 Distributed Solutions .................. ....... 13

3 THE MELSeM PROTOCOL .................. ......... 17

3.1 Bloom Filter .. .. .. ... .. .. .. ... .. .. .. .. ... ..... 17
3.2 MELSeM Protocol Outline .................. ........ 18
3.3 M ELSeM Details .................. ............. 19
3.4 Impact of false positive ................... . . 21

4 SIMULATIONS AND ANALYSIS ........... . . 22

4.1 Memory Performance .................. ........... 22
4.2 Energy Performance .................. ........... 26
4.3 Detection Probability .................. ........... 26

5 CONCLUSION .................. ................. 28

5.1 Future W ork Scope .................. .......... .28
5.2 Sum m ary .................. ................. 29

REFERENCES ... ............ ............... .... 30

BIOGRAPHICAL SKETCH ........... ........ . 32










LIST OF FIGURES


Figu

3-1


page


claim in two


re

A simplified example of mapping of ID and location of a node's
distinct Bloom filters stored at each intermediate node .. ...

An example of replica detection process used in MELSeM .

An example of 5'`. incremental sub areas .. ...........

Average memory consumption .. ................

Maximal memory consumption .. ..............

Average memory distribution .. ...............

Detection Probability of LSM and MELSeM in a uniformly depl
with different node densities .. ................


oi, square area









Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

MEMORY EFFICIENT DISTRIBUTED DETECTION OF NODE REPLICATION
ATTACKS IN WIRELESS SENSOR NETWORKS

By

Vishal Khanapure

August 2009

('C! ,i: Shigang C (',
Major: Computer Engineering

Low cost availability of sensor nodes makes them an attractive choice for sensor networks

and their applications. To keep the costs low, sensor nodes are generally unshielded. This

unshielded nature of sensor-network nodes combined with their ease of deployment, makes

them vulnerable because an adversary can capture these nodes, copy security information to

make replicas and deploy the replicas in the network to render malicious attacks. Replication

attacks can be extremely hazardous to a network if done in a strategic way. For any node

replication detection protocol, the three most important design issues are memory usage,

detection probability and energy consumption. Previous node replication detection schemes

either incur large memory overhead or consume excessive energy, particularly in the central

region of the network. This thesis presents a Memory Efficient Line-Selected Multicast

(\l I.tSeM) algorithm which uses efficient bloom filter data structure. We propose a novel

distributed technique for detecting node replication attacks using MELSeM. MELSeM reduces

the average memory overhead of the network by nearly 7ii' than the previous distributed

schemes while achieving nearly same detection probability.









CHAPTER 1
INTRODUCTION

A Wireless Sensor Network (WSN) is a network of wireless sensor nodes or devices

which work cooperatively to achieve a common purpose. There are numerous applications of

WSNs ranging from military use and surveillance, to civil use. The low cost availability and

ease of deployment of sensor nodes makes them an attractive choice for these applications.

Furthermore, these networks are highly scalable as adding and removing new nodes to them

is fairly simple. New nodes can join such a network without administrative intervention

or without communication with a central authority such as a base station. These nodes

only need to initiate a neighbor discovery protocol [7, 13] by broadcasting their pre-stored

credentials.

Security is one of the key concerns for the proper functioning of WSNs, especially in

military applications in which sensor nodes are deploy, l1 in enemy territory to carry out

critical functions [3]. To be able to produce sensor nodes at low costs they are not usually

provided with tamper-proof hardware or shielding that can detect pressure, voltage and

temperature changes [11, 20, 22]. The unshielded nature of the nodes can be exploited by

an adversary to access a sensor's internal state. If an adversary is able to capture a sensor

node and extract its encryption/authentication keys, it can copy those keys to other generic

nodes to create several replicas and insert them into the network at strategic locations,

which is commonly known as node replication attack. Node replication attacks can render

the network susceptible to large class of harmful attacks [4, 6]. If the replicas are placed at

wisely chosen locations, they can revoke legitimate nodes, inject false data, spy for critical

information and may even disconnect the network by invoking node revocation protocols

that are based on threshold voting schemes [7, 10, 13, 17].

The main technical challenge in detecting node replication attacks arises from the memory

constrained nature of sensor nodes. If a single authentication key is being used at two or

more distinct locations in the network, it means there has been replication. An effective









solution must be able to detect any such occurrence, with high probability. This kind of

detection requires comparison of authentication information on a network-wide scale. The

limited memory of sensor nodes, which is usually less than 10K of RAM [1] for low-end

sensors, restricts the amount of authentication information that can be stored at each node.

The limited battery life of sensor nodes, also restricts the amount of energy that can be spent

on replication detection. Thus memory efficiency, energy efficiency and detection probability

are the principle criteria for detection of replication attacks.

Previous schemes to detect node replication attacks either incur high memory overhead or

high energy overhead or both. The first solutions for replication detection include centralized

schemes [13] and localized voting protocols [7]. However, the former usually rely on a base

station (BS) and have the problem of single point of failure; the latter cannot deal with

distributed node replication attacks, in which replicas are placed at least two hops away

from each other. A fully distributed solution is needed that can detect replicas anywhere in

the network and yet incur small memory and energy overhead.

In [18], Parno et al. proposed a distributed detection scheme called Line-Selected Multicast

(LSM) which utilizes network topology to select witness nodes for a node's location and

exploits geometrical properties of the network to detect replicas. In LSM, for each node in

the network a digitally-signed location-claim is generated and stored along randomly chosen

k line segments. Usually a unique key is used to sign each location which is stored at that

location. When a node replica is created it uses the same key as the node to sign a different

location where it is placed. Parno et al. [18] show that when k is sufficiently large, the line

segments of two conflicting location claims (signed with the same key for distinct locations)

intersect with a high probability. The replication attack is detected at the intersection node

since it can see both the conflicting location claims i.e. two locations for the same node.

Though LSM provides a distributed solution, it has its own drawbacks. It is shown through

simulations in Chapter 4 that in a network of n nodes, in LSM, each node has to store

O(kvn) location claims which can easily exhaust the limited memory of sensor nodes.









In this thesis, we propose Memory Efficient Line-Selected Multicast Protocol (\! I'I.SeM)

for distributed detection of node replication attacks, which is based on LSM. We use bloom

filter data structure and emergent properties (the properties that are achieved only through

the collective action of multiple nodes) [14] to accomplish our algorithm. MELSeM reduces

the number of location claims stored at each sensor node to O(k) from 0(kvn) in LSM. The

basic idea of MELSem is to encode the location-claim information into two compact Bloom

filters instead of storing the actual location-claim at each node. Only select few nodes,

which are the witnesses of the node store its actual location-claim. With these memory

savings, we have designed a novel distributed technique to detect node replication attacks.

We evaluated MELSeM through extensive simulations and the results show that MELSeM

reduces the memory overhead of a network up to 7i' on an average and yet achieves nearly

same detection probability as LSM.

The rest of the thesis is organized as follows: background and related work is discussed

in C'!i lpter 2. The MELSeM protocol is presented in ('!i lpter 3. In C'!i lpter 4, we present

our simulation results and compare MELSeM with LSM. Finally, in ('! Ilpter 5 we discuss

future work directions and wrap up.









CHAPTER 2
BACKGROUND AND RELATED WORK

2.1 Background

In this section we discuss the characteristics of sensor nodes that make them vulnerable

and susceptible to attacks, followed by node replication attacks. We explain how a node

replication attack can be launched and list its consequences.

2.1.1 Sensor Networks

A typical large scale sensor network usually consists of many low-cost, low-end sensor

nodes. Each of these nodes in a network has a CPU and around 10KB of RAM [1].

Due to the processing capability of the sensor nodes, numerous applications are developed

based on them, especially military applications. Some other examples of these applications

include burglar alarms, emergency response, habitat monitoring, battlefield surveillance,

home automation and traffic control. The typical characteristics of low cost sensor nodes

include no shielding or protection li1,. lriii and limited battery life. Another important

characteristic of sensor nodes is that they are easy to deploy. They can be added to a

network without administrative supervision. Due to these characteristics they are susceptible

to attacks from an adversary.

2.1.2 Node Replication Attacks

Replication attacks are easy to launch on sensor networks because of the ease of deployment

and unshielded nature of sensor nodes. An adversary would only need to capture one node.

As these nodes have no shielding, the adversary will be able to extract the captured node's

secrets, transfer these secrets to generic nodes and deploy the clones. The consequences of

clone attacks can be hazardous. This is because a malicious clone knows every secret that

the compromised node knew. An adversary can use this to his advantage and inject false

data, suppress legitimate data, perform malicious activities in the network, blame innocent

nodes for malicious activities, revoke legitimate nodes by using -- related voting, monitor









all the communication going on in the network and may even be able subvert the entire

network.

2.2 Models

In this section we describe the network model and the adversary or threat model used in

MELSeM protocol which are similar to those used in [9, 18].

2.2.1 Network Model

For simulations and for execution of MELSeM protocol we consider a large sensor network

deploy, .1 in a hostile environment in which sensor nodes are uniformly deploy, .1 After

deployment these nodes remain relatively stationary. Each of these nodes knows its own

geographic location as well as the locations of its neighbors. Due to this knowledge of the

nodes, geographic routing [15] is possible in which, a packet can be routed hop by hop until

it reaches the destination. Similar to [18] we also assume that clocks of sensor nodes are

loosely synchronized [12] and identity based public key system [8, 19] is used. Each node, by

using its private key, can establish a pairwise secret with its neighboring nodes for mutual

authentication and also can produce a digital signature that can be verified by other nodes

in the network.

The location claim of a node a is represented as Ca = (IDa, la, [H(ID,, la)]K,-), where

IDa is its unique ID, 10 is its location, H is a hash function and K-1 is its private key.

A node which stores the complete location claim Ca for node a and which can verify the

identity of a is called its witness node.

2.2.2 Adversary Model

We assume that the adversary has the ability of compromising only a few sensor nodes.

This is because an adversary that can capture many nodes can obviously break any protocol

running in the network. The adversary, after capturing a few nodes, can launch arbitrary

attacks on the network including node replication attack. Similar to [9, 18] we make the

assumption that any cloned node has at least one legitimate neighboring node. We also

assume that the nodes under enemy control can communicate and collaborate with other









nodes in the network. Furthermore, we assume that the adversary operates in a covert way

in order to avoid detection.

2.3 Related Work

In this section we discuss the existing solutions for detecting node replication attacks,

analyze them and point out their limitations.

2.3.1 Centralized Solution

In the Centralized Detection [13] scheme, each node sends a list of its neighbors and their

location claims, to a central authority like a Base Station. The base station searches for

the lists of duplicate claims and finds existing conflicts. The disadvantage of this approach

is that, there is a single authority to perform this checking. If this authority fails or is

compromised, the entire network can be compromised. Thus it suffers from the well known

single point of failure problem. The other disadvantage is that some applications may not

use base stations at all. Furthermore, in this approach the nodes near the base stations get

exhausted sooner and become attack targets.

2.3.2 Localized Solution

In the Localized Detection [7] scheme, neighbors of a node use voting protocols and come

to a conclusion about the authenticity of a node. The principal drawback of this approach

is that replication is a global event and cannot be detected if done just locally, which means

that this method fails to detect replicas that are two hops away from the locality.

2.3.3 Distributed Solutions

The existing solutions for distributed detection of node replication attacks such as [9,

18, 23] require each node in the network to sign its actual location with its private key in

a location claim. The correctness of a claim and the signature of the node is verified by

its neighboring nodes. If a node refuses to provide its location claim, the neighbors of the

node cut it off from the network and deny any communication with it. To detect replication

attacks we check if the same private key is used to sign two or more location claims. If a

private key has been used more than once we conclude that the node which owns the private









key was compromised to produce malicious replicas. In order to prevent replication attacks

and malicious nodes from entering the network, the above detection process is carried out

periodically. Any new genuine nodes which want to enter the network or existing nodes

which want to relocate to some other place in the network, also have to go through this

detection process, so as to prevent insidious replicas from becoming a part of the network.

In the distributed solutions based on location claims the identity-based public key system

[8, 19] is used in which every node stores its own private I. ;, and a master public I ;1

The private key of a node is computed using its node ID and a master private I. ;, This

computation is done before the node is deploy, ,1 in the network. The master private key is

kept secret and not loaded on any of the nodes. To verify the signature of a location claim,

only the public key of the node which produces the claim needs to be computed. The public

key of a node can be computed using its node ID and the master public key. Location-claims

are said to be conflicting with each other if they have same node ID and signature but their

physical locations are different. As mentioned in subsection 2.2.2, an adversary has limited

control, hence it will not be able to produce a valid pair of node ID and private key which

can potentially generate a verifiable signature, without the knowledge of the master private

key. Thus the only option an adversary has is to produce replicas by (. ., iing the private

keys and node IDs of compromised nodes on to generic nodes.

One basic difference between the existing distributed solutions for detecting node replication

attacks is the manner in which they store location claims, which has a significant impact

on the memory and communication overhead of the network. The simplest solution is to

use a network wide broadcast protocol where each location claim is broadcast to every

node in the network and stored at each of them. Although this method can achieve 10it' .

accuracy and detection probability, it is not feasible since it incurs tremendous memory and

communication overhead. Another solution is to store the location claim of a node at a

pseudo-random location in the network that is determined by the ID of the node. In this

solution, the conflicting claims would be forwarded to the same location for verification. The









problem with this approach is that when an adversary captures a node, it would be able

to determine the location to which the conflicting claims go based on the node's ID. If the

adversary compromises that location or if it jams that portion of the network, the detection

of replication would fail and the adversary would be able to produce as many replicas as he

wants. [18]

To solve this problem of predictability, one solution is to make the location where a claim

will be stored unpredictable. Which implies we store each claim at random unpredictable

places in the network. Parno et. al. in [18] used this approach and proposed two distributed

solutions namely Randomize Multicast (RM) and Line-Selected Multicast (LSM) that store

claims at random locations in the network. In RM, each location claim is stored at O(V )

randomly selected witness nodes (discussed in 2.2.1), where n is the number of nodes in the

network. The birthdlil paradox [16] assures that a common witness node will receive any two

conflicting claims with a high probability. This witness node will be able to detect replication

attack since it would have two claims with same IDs but with different locations. The witness

node will then broadcast the claim to the entire network informing that the node to which

the claim belongs has been compromised. The compromised node and all its replicas with

the same ID are then cut off from the network. The problem with RM protocol is that

each node has to store O(Vn) claims on average and the communication requirement of the

network is O(n2). The second approach, LSM, reduces the communication overhead of the

network compared to RM. In LSM, a node's claim is stored at all intermediate nodes along

different paths called line segments from a node to its witness nodes. The main idea of LSM

is that the line segments from two conflicting claims will have high probability to intersect

at a node which is on both line segments. This node will be able to detect replication attack

since it will have both the claims i.e. it will see two claims with same node ID but different

locations.

Although LSM reduces the communication overhead in RM, it still has its own drawbacks.

First problem is that each node in LSM is required to store O(kv ) claims where k is the









average number of line segments for each claim. For high detection probability the value

of k should be reasonably high, such as six used in [18]. Moreover, since we know that the

security of a digital signature depends on its size, the size of each location claim should be

reasonably large to achieve high-level security. Usually, the size of a claim would be more than

40 bytes since digital signature requires 40 bytes according to DSS [2]. The memory of sensor

nodes is limited and it is required to perform many other functions such as communication,

measurements and computations. This means that storing O(kvn) location claims at each

sensor node could pose a serious concern, especially when n is large. Another problem of

LSM arises from the fact that it uses random line segments. It is known that random line

segments tend to pass through the central region more frequently than the outer region in

a convex deployment area [9]. Thus a node in the central region of the network area would

have to store much more claims than the nodes at the periphery. This storage requirement

can be so high for the nodes at the center that many of them might crash just because of

memory overflow.

The goal in this thesis is to reduce the memory overhead of the network. We use an

approach similar to LSM while devising a novel distributed protocol using efficient Bloom

filters for detecting node replication attacks. In C'i plter 3 we describe our MELSeM protocol

in detail.









CHAPTER 3
THE MELSEM PROTOCOL

We have used Bloom filter data structure in MELSeM protocol. So we discuss Bloom

filter in the next section to lay the foundation for understanding MELSeM. We give the

outline and basic idea behind MELSeM in section 3.2 and describe MELSeM protocol in

detail with an example in section 3.3.

3.1 Bloom Filter

Bloom Filter Definition: "A Bloom filter is a simple space-efficient randomized data

structure for representing a set in order to support membership queries" [5].

The Bloom filter begins as an array of all Os. The purpose of this filter is to support

membership queries by indicating the presence or absence of an element in a set. Whenever

a new item z is to be added to a set, it is hashed u times using u different hash functions,

where u can vary as per need. Each of these u hashes result in a bit location in the array

which is set to 1. To test the membership of an element z' in the set, similar hashing is

repeated and the corresponding bits are checked in the array. If all these bits have been

set to 1, the element might be present in the set. Note that we are -Z.iing that the element

might be present in the set because the corresponding bits might be set to 1 as a result of

insertion of other elements in the set. This property of Bloom filter where it indicates that

an element is present in the set when it is actually not present is called false positive. The

probability of false positive PB for a Bloom filter as per [5] is -




PB = ( -(- u)u (1 -e -)u. (3-1)

where s is the number of elements in the set, u is the number of hash functions used and

m is the number of bits in the Bloom filter array. Another interesting property of Bloom

filter is that it has no false negative i.e. if it represents that an element is not present in the

set, that means that the element is actually not present in the set and is 10('. accurate.









H1(ID) H2(ID) H3(ID)


1 0 a1 0 i 0 1 1 010101
H4(Location) H5(Location) H6(Location) H7(Location)


110 10 1

Figure 3-1. A simplified example of mapping of ID and location of a node's claim in two
distinct Bloom filters stored at each intermediate node.


3.2 MELSeM Protocol Outline

In MELSeM, each node in the network has two Bloom filters in it. Two types of nodes

are involved in the determining whether a node in the network is legitimate or a replica.

These nodes are called witness nodes and intermediate nodes. Witness nodes, as discussed

in 2.2.1 store the complete location claims while intermediate nodes are the nodes which

store only the Bloom filter representations. For storing these Bloom filter representations,

the intermediate nodes insert any node a's ID and location into its two Bloom filters which

are known as the ID filter and the location filter respectively. Even though the intermediate

nodes do not store the complete copy of the claim, they have enough information in their two

Bloom filters to tell whether they have seen a claim previously. Figure 3-1 shows a simplified

example of mapping a node's ID and location in two Bloom filters. The upper Bloom filter

is similar to ID filter and lower Bloom filter is similar to location filter used in MELSeM.

The MELSeM protocol outline is as follows Initially, the location claim Ca of a node a

is multicasted to a number of randomly-selected witness nodes in the network via its one-hop

neighbors using geographic routing. A one-hop neighbor / of a has a certain probability p

to participate in the multicast. If it participates, it becomes one of the witness nodes for a

and continues to forward the location claim Ca to a randomly-selected location. The node

closest to this randomly selected location receives the location claim C, and becomes another

witness node w by storing C,. All the intermediate nodes on the routing path P from / to

w store just the Bloom filter representations of IDa and la in their ID filters and location









filters respectively. The information stored in the Bloom filters helps the intermediate nodes

to detect a conflicting claim C. Upon seeing a conflicting claim C', a revocation protocol is

invoked and the intermediate node which detects this conflict forwards C'7 along the routing

path P so that it reaches Q and/or w. When a witness node Q or w receives a conflicting

claim C', for the node a it broadcasts Co and C' to the entire network so that node a and

all its replicas can be revoked.

3.3 MELSeM Details

Every node a in the network must broadcast its location claim C, to its one hop neighbors

at the beginning of every detection period. If it refuses to broadcast its location claim, it is cut

off from the network by all its one hop neighbors by refusing to collaborate or communicate

with it. Whenever a neighboring node Q receives C, it tests the correctness of la and verifies

the signature present in the claim. When Ca is verified and found to be valid, Q makes itself

a witness node for a with a certain probability p and forwards Ca to a random location oldest

in the network.

On the forwarding path from 3 to oldest whenever any node 7 receives Ca, a two-phase

conflict check is performed by it to test if any conflicting claims can be detected. In the

first phase, the signature present in Ca is verified by 7. If the location claim appears to be

invalid or fake, it is dropped immediately. Then 7 being a witness node for other nodes in

the network, it compares Ca with the location claims stored locally with it. If 7 finds a claim

conflicting with Ca, a replication attack is detected. It then invokes a revocation protocol,

in which the conflicting claims are forwarded to the witness nodes. The first witness node to

receive this information broadcasts the conflicting claims to the entire network so that the

replication attack can be taken care of. If node 7 does not detect any conflicting claims, the

MELSeM protocol proceeds to phase two.

In phase two, there are two possible cases. The first case is where node 7 finds mapping

for IDa in its ID filter while there is no mapping for la in its location filter. This means









w
O Replicated Nodes
Q Intermediate Nodes
... *. Witness Nodes



aa

Figure 3-2. An example of replica detection process used in MELSeM


that there are conflicting representations in the filters. It implies that a conflicting claim C'

with the same IDa and a different location Ia has gone via 7 and left its trail in the filters.

In the second case of phase two, when the node 7 does not find IDa in its ID filter or la

in its location filter, the newly arrived claim Ca is treated as a legitimate claim. This claim is

approved of having passed the two-phase conflict check. Then 7 tries to forward Ca towards

Widest. If there are no other nodes close to Idest, 7 stores the location claim in its memory

and acts as a witness node itself. Else if there is another node closer to Idest, 7 forwards the

claim to that node and acts as an intermediate node by making appropriate entries in its

two Bloom filters for IDa and la. This explains the working of MELSeM protocol.

Figure 3-2 shows an example of how replica's are detected in MELSeM. A location claim

forwarding path is shown using solid arrows while the dashed arrows show the action taken

by an intermediate node 7 upon replica detection. Node 7 initiates a revocation protocol by

locally broadcasting Ca and C' to its one-hop neighbors. Among these neighbors, the ones

that find conflicts with Ca or C' in their Bloom filters continue this one-hop local broadcast

until the claim eventually reaches one of the witness nodes, which then notifies the entire

network about the replication attack.

It is worth noting that during the revocation, not just the replica node but also the

legitimate node that has been compromised is removed. The reason for this is that, a

compromised node can be duplicated any number of times and we want to prevent it.









3.4 Impact of false positive

As MELSeM uses Bloom filters, the false positive aspect of Bloom filters must be taken

into account and the impact of false positive on the working of MELSeM must be considered.

In MELSeM, when 7 receives Ca for the first time, it should not have mappings for IDa and

la in its Bloom filters. But due to false positive, it is possible that IDa might be present

in the ID filter. If la is not present in the location filter, 7 will erroneously term Ca as a

conflicting claim. This leads to false initiation of the revocation protocol. The probability

of this false initiation of revocation, Pf is -



Pf = PB(1 PB) M (1 -(1 ( e )) (3-2)

The probability of false initiation of revocation can be made negligible by adjusting

various parameters. For example by increasing the number of bits m in the Bloom filter

array or by increasing the number of hash functions u used etc. We have found through

extensive simulations that this probability is so small that it hardly ever causes the MELSeM

protocol to falsely report a replication attack.









CHAPTER 4
SIMULATIONS AND ANALYSIS

Simulation Settings

In order to simulate LSM and MELSeM protocols we consider a 1000 x 1000 unit square

area in which n nodes are deploy, 1 uniformly with n ranging from 1000 through 10000.

Similar to [18] we assume bidirectional communication model with links between one-hop

neighbors. We select transmission range such that each node has approximately 20 neighbors.

Location claims are forwarded by simulating a simplified geographic routing protocol [15]

where a node greedily forwards a location claim to the neighbor closest to the destination.

When a node finds no node closer to the destination, forwarding is stopped. We use six

line segments for LSM (as originally used in [18]) as well as for MELSeM. We compromise a

random node in the network and insert its one replica at a random location in the network.

We also consider the cases for multiple replicas. We create 100 random network graphs for

each network size and calculate the average for results.

For our simulations we have assumed that the size of each location claim is 46 bytes

where 2 bytes are for ID, 4 bytes for location (x and y coordinates) and 40 bytes for the

digital signature as per DSS [2]. We adjust the number of bits m and number of members

s to be inserted in the Bloom filter such that m/s = 15. This means we use 15 bits; along

with 7 hash functions u to store an element in a Bloom filter.

Figure 4-1 shows a square deployment area which is divided into 20 equal sub-areas. Each

of these sub-areas accounts for 5'. of the deployment region. The numbering of sub-areas

is done from 0 through 19, with the central sub-area being number 0 and the outermost

sub-area being 19.

4.1 Memory Performance

Sensor nodes have limited memory usually 4KB 10KB [1], so it is a critical resource.

A sensor node has to perform various functions in a networks such as communication,

measurements, collaboration, computations etc. Thus the memory that might be available






























Figure 4-1. An example of 5'. incremental sub areas


for security purposes including replication detection can be very limited. The basic difference

between LSM and MELSeM is that in LSM every node in the network stores a complete

copy of the location claim whereas in MELSeM only witness nodes store the complete i'v of

location claim. The rest of the nodes in MELSeM store only the Bloom filter representations

of the location claims that pass through them. Storing several hundreds of complete location

claims will lead to memory overflow of nodes in LSM, which might cause the nodes to crash.

The efficient use of Bloom filters in our protocol drastically reduces the memory requirement

of the network, preventing the nodes from memory overflows and crashing.

Figure 4-2 shows average memory consumption by LSM and MELSeM for different

network sizes. As it can be observed, the memory consumption increases as network size

increases. This is because each node in the network has to store more information either in

the form of location claims or Bloom filter representations. MELSeM reduces the memory

overhead of the network by nearly 71' compared to LSM.

Figure 4-3 shows the maximal memory consumption for LSM and MELSeM. Maximal

memory consumption is the maximum amount of memory consumed by a node in the













3500

3000

2500

2000

1500

1000


500


1000 2000 3000 4000 5000 6000 7000 8000 900010000
Number of nodes

Figure 4-2. Average memory consumption


30000


25000


20000


15000


10000


5000


0


1000 2000 3000 4000 5000 6000 7000 8000 900010000
Number of nodes


Figure 4-3. Maximal memory consumption


LSM 1
MELSeM -x






-





._--x-----x----
.. ..- .X .X. .. .X . .- X .

I-









10000 ..
9000 S
9000 MELSeM
S 8000
7000 -
S 6000
S 5000 -
S 4000 -
S 3000 -
2000 -
1000 --
0 1I I I I I I I
0 2 4 6 8 10 12 14 16 18 20
Number of sub-area

Figure 4-4. Average memory distribution

network. It is clear from the figure that for most network sizes the maximal memory

consumption of LSM exceeds 10KB which can easily exhaust the memory capacity of low-end

sensor nodes. In this scenario, MELSeM stands out, which can reduce the memory overhead

of the network up to 85.

Figure 4-4 shows the distribution of memory consumption for LSM and MELSeM in a

network of 5000 nodes. As described in simulation settings at the beginning of this chapter,

the network is divided into 20 equal sub-areas as shown in 4-1. It is quite clear that LSM

consumes a lot more memory in the central sub-areas than in the outer sub-areas. On

the other hand, MELSeM balances the memory consumption across all sub-areas evenly.
This is because LSM has more witness nodes (storing complete copies of location claims)

concentrated in the central region of the network whereas in MELSeM witness nodes are

evenly spread across the network. MELSeM also has many intermediate nodes concentrated

in the central region, however their cumulative memory consumption is not too high because

they use space efficient Bloom filters instead of storing the complete claims.









4.2 Energy Performance

The energy consumption of the network is measured by counting the number of messages

sent and received by each node. We use the energy model from [21] in which a new node has

a total of 324,000 mJ of available energy. Bit sending costs 0.059 mJ and bit receiving costs

0.028 mJ. From our simulations we found that, MELSeM incurs approximately ;:' more

energy consumption on average than LSM. This is because in LSM, any intermediate node

which detects replication can itself broadcast both the conflicting claims to the network since

it has the complete ( .i,- of location claim. Whereas in MELSeM, extra communication is

required for the revocation protocol in which, the intermediate node sends the conflicting

claims to the witness nodes since the intermediate node itself doesn't have the complete copy

of location claim. Though the energy consumption of MELSeM is slightly higher than LSM,

it is quite acceptable considering the amount of memory savings achieved through MELSeM.

In terms of communication, since we assume that the length of one line segment is O(Vn)

and there are k line segments drawn for each node, for the whole network the number of

messages sent and received are both O(kn v) in MELSeM as in LSM.

4.3 Detection Probability

We denote detection probability, the probability to detect node replication attack in

one detection period, as Pd. A larger value of Pd implies greater accuracy in detecting

replication attacks. Figure 4-5 shows the detection probability of LSM and MELSeM for a

uniformly deploy, -l square area with node densities ranging from 1000 through 10000. It

is clear from the figure that Pd ranges from 85'. to 95'. for both LSM and MELSeM and

is approximately same for both protocols. One might think that increasing the number of

replicas in the network is harmful, but it actually improves the detection probability since

there will more line-segments in the network which increase the chances of conflicting claims

being caught.

























Detection probability in a uniformly distributed square area

1 I I MELSeM
LSM



0.8





0.6




0.4





0.2
00





0 -- -- - -- -- - -- --
1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
Number of nodes



Figure 4-5. Detection Probability of LSM and MELSeM in a uniformly deploy, 1 square
area with different node densities









CHAPTER 5
CONCLUSION

5.1 Future Work Scope

The primary focus of the approach in this thesis is to reduce the memory requirements of a

sensor network and still be able to effectively detect node replication attacks. The MELSeM

protocol proposed, induces slightly higher communication cost on the network compared to

LSM since an instance of replication detection has to be propagated till the witness node

which has the location-claim and can take corrective action. This additional communication

leads to more energy consumption. A future enhancement would be to reduce the energy

consumption of the network as much as possible.

In LSM as well as in MELSeM, k random line segments are used to store location-claim

information. When line segments are drawn at random in a convex area, they pass through

the central region more frequently than other regions. So the network will have higher

node density in the central region of the network. These nodes in the central region of the

network incur higher memory and energy overhead than the nodes at the periphery. This

is called the crowded center problem Conti et al. [9] solve the crowded center by the use

of a periodically renewed pseudo random number on a network wide scale. However, the

infrastructure required for this solution may not be alv--, available in the network. To

reduce the energy and memory burden on the nodes in the central region of the network,

without relying on additional infrastructure can be an interesting area for future research.

The other problem with line segments method is that, when two line segments intersect

they might not intersect at a node. When such an intersection happens, no common node

has sufficient information to detect node replication attack. Further research to handle such

occurrences can be done.

The node replication detection probability of MELSeM is similar to that of LSM and is

in the range of 85-95' Although this amount of accuracy is not bad, it is desirable to attain

a detection probability and accuracy of 10' in the future.









5.2 Summary

Sensor networks are susceptible to node replication attacks. The memory of sensor nodes

is limited and valuable. MELSeM protocol proposed in this thesis, used storage efficient

Bloom filter data structure and devised a novel algorithm for detecting node replication

attacks. MELSeM provides distributed detection of node replication attacks in sensor

networks and reduces the average memory overhead of the network by nearly 7i' than

the previous distributed schemes. This saves the valuable memory of sensor nodes which can

be used for other meaningful data.









REFERENCES


[1] Sensor node. [internet] Wikipedia; [updated 'ii,' June 29; cited ',,,"' June 30]. Available
from: http://en.wikipedia.org/wiki/Sensornode.

[2] Digital signature standard. FIPS PUB 186-3, March, 2006.

[3] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor
networks:a survey. International Journal of Computer and Telecommunications Net-
working Elsevier, 38(4):393-422, Mar.2002.

[4] A. Becher, Z. Benenson, and M. Dornseif. Tampering with motes:real-world physical
attacks on wireless sensor networks. In Proceedings of the 3rd International Conference
on S.. ;',./; in Pervasive Corn,,';/.:., (SPC), pages 104-118, 2006.

[5] A. Broder and M. Mitzenmacher. Networking applications of bloom filters: A survey.
In Proceedings of Allerton Conference, 2002.

[6] S. Capkun and J.P. Hubaux. Secure positioning of wireless devices with application to
sensor networks. In INFOCOM, pages 1917-19 '., 2005.

[7] H. C'!i ini A. Perrig, and V.D. Song. Random key predistribution schemes for sensor
networks. In Proc. of IEEE Symposium on S.. .'; and Pr''. ,;l (S&P'OS), AM ,i.2003.

[8] C. Cocks. An identity based encryption scheme based on quadratic resides. In Pro-
ceedings of the 8th IMA International Conference on Cril,., 'I',i',l,; and Coding, pages
360-363, London, UK, Springer-Verlag., 2001.

[9] M. Conti, R.D. Pietro, and L.V. Mancini. A randomized, efficient, and distributed
protocol for the detection of node replication attacks in wireless sensor networks. In
Proc. of the 8th ACM' International Symposium on Mobile Ad Hoc Networking and
Comput- ing (_[obiHoc'07), pages 80-89, 2007.

[10] J.R. Douceur. The sybil attack. In Proceedings of Workshop on Peer-to-Peer S1,4i.
(IPTPS)., Mar.2002.

[11] J. Dyer, M. Lindemann, R. Sailer, L. Van Doom, S.W. Smith, and S. Weingart. Building
the ibm 4758 secure coprocessor. IEEE Computer, 2001.

[12] J. Elson, L. Girod, and D. Estrin. Fine-grained network time synchronization using
reference broadcasts. SIGOPS Operating S,.1 m, Review, 36(SI):147-163, 2002.

[13] L. Eschenauer and V. Gligor. A key-management scheme for distributed sensor
networks. In Proc. of the ACM' Conference on Computer and Communication Secu-
i;:/, (CCS), Nov.2002.

[14] V.D. Gligor. Security of emergent properties in ad-hoc networks. In Proc. of Interna-
tional Workshop on S,. iii.; Protocols, Apr.2004.









[15] B. Karp and H. T. Kung. Gpsr: Greedy perimeter stateless routing for wireless networks.
In Proceedings of the 6th Annual AC I /IEEE International Conference on Mobile Com-
puting and Networking (1[obiCom '00), pages 243-254, 2000.

[16] A.J. Menezes, S.A. Vanstone, and P.C.V. Orschot. Handbook of applied cryptography.
CRC Press, Inc., 1996.

[17] J. N, v.- i,,-, E. Shi, D. Song, and A. Perrig. The sybil attack in sensor networks:
Analysis and defenses. In Proceedings of IEEE Conference on Information Processing
in Sensor Networks (IPSN)., Apr.2004.

[18] B. Parno, A. Perrig, and V.D. Gligor. Distributed detection of node replication attacks in
sensor networks. In Proc. of 2005 IEEE Symposium on i';, and Pr':; ;, (SiP'05),
pages 49-63, Washington, DC, USA, 2005.

[19] A. Shamir. Identity-based cryptosystems and signature schemes. In Proceedings of
CRYPTO 84 on Advances in cr;,l 'i.1. i;, pages 47-53, Springer-Verlag New York, Inc.,
1985.

[20] S.W. Smith and S. Weingart. Building a highperformance, programmable secure
coprocessor. Computer Networks, Special Issue on Computer Network S,. ,I
Apr.1999.

[21] A. Wander, N. Gura, H. Eberle, V. Gupta, and S. C. Shantz. Energy analysis of
public-key cryptography for wireless sensor networks. In Proceedings of the Third Annual
IEEE International Conference on Pervasive CornT,,i,.,: and Communications (PER-
COM '05), pages 324-,- .:', 2005.

[22] S. Weingart and S. Weingart. Physical security devices for computer subsystems: A
survey of attacks and defenses. In Cr;,l 'l,i',''l,'.: Hardware and Embedded Sii 14.
(CHES)., Aug.2000.

[23] Bo Zhu, Venkata Gopala Krishna Addada, Sanjeev Setia, Sushil Jajodia, and Sankardas
Roy. Efficient distributed detection of node replication attacks in sensor networks.
Computer S. .: Applications Conference, volume 0, pages 257-267, issn 1063-9527,
Los Alamitos, CA, USA, 2007.









BIOGRAPHICAL SKETCH

Vishal is from the city of Latur, which is in Maharashtra state of India. He did his

bachelor's in computer science and engineering from Government College of Engineering-Aurangabad

(2005). He worked with Cognizant Technology Solutions Pvt. Ltd in Pune as a Programmer

Analyst for one and a half years. He did his master's in computer engineering from the

Computer and Information Science and Engineering Department at the University of Florida

(2009). His research interests include Network & Systems Security, Quality of Service in

Wireless Networks, Internet protocols, Distributed Systems and Secure Embedded Systems.





PAGE 1

1

PAGE 2

2

PAGE 3

3

PAGE 4

Firstandforemost,IthankDr.Chenforhisinvaluableguidance.Withouthisencouragementthisthesiswouldnothavebeenpossible.MysupervisorycommitteemembersDr.PrabhatMishraandDr.AlinDobraprovidedeectivepositivecommentsandsuggestionsforwhichIamgratefultothem.Iamfortunateforhavingextremelylovingandcaringparents.Theyhavebeenasteadfastsupportallthroughmyeducationandhaveconstantlybeenasourceofmotivation.Iamalsothankfultomyfriends.EspeciallyAparnaVenkatesan,RebeccaDavidandMingZhangfortheirsuggestions. 4

PAGE 5

page ACKNOWLEDGMENTS ................................. 4 LISTOFFIGURES .................................... 6 ABSTRACT ........................................ 7 CHAPTER 1INTRODUCTION .................................. 8 2BACKGROUNDANDRELATEDWORK ..................... 11 2.1Background ................................... 11 2.1.1SensorNetworks ............................. 11 2.1.2NodeReplicationAttacks ........................ 11 2.2Models ...................................... 12 2.2.1NetworkModel ............................. 12 2.2.2AdversaryModel ............................ 12 2.3RelatedWork .................................. 13 2.3.1CentralizedSolution ........................... 13 2.3.2LocalizedSolution ............................ 13 2.3.3DistributedSolutions .......................... 13 3THEMELSeMPROTOCOL ............................ 17 3.1BloomFilter ................................... 17 3.2MELSeMProtocolOutline ........................... 18 3.3MELSeMDetails ................................ 19 3.4Impactoffalsepositive ............................. 21 4SIMULATIONSANDANALYSIS .......................... 22 4.1MemoryPerformance .............................. 22 4.2EnergyPerformance .............................. 26 4.3DetectionProbability .............................. 26 5CONCLUSION .................................... 28 5.1FutureWorkScope ............................... 28 5.2Summary .................................... 29 REFERENCES ....................................... 30 BIOGRAPHICALSKETCH ................................ 32 5

PAGE 6

Figure page 3-1AsimpliedexampleofmappingofIDandlocationofanode'sclaimintwodistinctBloomltersstoredateachintermediatenode. .............. 18 3-2AnexampleofreplicadetectionprocessusedinMELSeM ............ 20 4-1Anexampleof5%incrementalsubareas ...................... 23 4-2Averagememoryconsumption ............................ 24 4-3Maximalmemoryconsumption ........................... 24 4-4Averagememorydistribution ............................ 25 4-5DetectionProbabilityofLSMandMELSeMinauniformlydeployedsquareareawithdierentnodedensities ............................. 27 6

PAGE 7

Lowcostavailabilityofsensornodesmakesthemanattractivechoiceforsensornetworksandtheirapplications.Tokeepthecostslow,sensornodesaregenerallyunshielded.Thisunshieldednatureofsensor-networknodescombinedwiththeireaseofdeployment,makesthemvulnerablebecauseanadversarycancapturethesenodes,copysecurityinformationtomakereplicasanddeploythereplicasinthenetworktorendermaliciousattacks.Replicationattackscanbeextremelyhazardoustoanetworkifdoneinastrategicway.Foranynodereplicationdetectionprotocol,thethreemostimportantdesignissuesarememoryusage,detectionprobabilityandenergyconsumption.Previousnodereplicationdetectionschemeseitherincurlargememoryoverheadorconsumeexcessiveenergy,particularlyinthecentralregionofthenetwork.ThisthesispresentsaMemoryEcientLine-SelectedMulticast(MELSeM)algorithmwhichusesecientbloomlterdatastructure.WeproposeanoveldistributedtechniquefordetectingnodereplicationattacksusingMELSeM.MELSeMreducestheaveragememoryoverheadofthenetworkbynearly70%thanthepreviousdistributedschemeswhileachievingnearlysamedetectionprobability. 7

PAGE 8

AWirelessSensorNetwork(WSN)isanetworkofwirelesssensornodesordeviceswhichworkcooperativelytoachieveacommonpurpose.TherearenumerousapplicationsofWSNsrangingfrommilitaryuseandsurveillance,tociviluse.Thelowcostavailabilityandeaseofdeploymentofsensornodesmakesthemanattractivechoicefortheseapplications.Furthermore,thesenetworksarehighlyscalableasaddingandremovingnewnodestothemisfairlysimple.Newnodescanjoinsuchanetworkwithoutadministrativeinterventionorwithoutcommunicationwithacentralauthoritysuchasabasestation.Thesenodesonlyneedtoinitiateaneighbordiscoveryprotocol[ 7 13 ]bybroadcastingtheirpre-storedcredentials. SecurityisoneofthekeyconcernsfortheproperfunctioningofWSNs,especiallyinmilitaryapplicationsinwhichsensornodesaredeployedinenemyterritorytocarryoutcriticalfunctions[ 3 ].Tobeabletoproducesensornodesatlowcoststheyarenotusuallyprovidedwithtamper-proofhardwareorshieldingthatcandetectpressure,voltageandtemperaturechanges[ 11 20 22 ].Theunshieldednatureofthenodescanbeexploitedbyanadversarytoaccessasensor'sinternalstate.Ifanadversaryisabletocaptureasensornodeandextractitsencryption/authenticationkeys,itcancopythosekeystoothergenericnodestocreateseveralreplicasandinsertthemintothenetworkatstrategiclocations,whichiscommonlyknownasnodereplicationattack.Nodereplicationattackscanrenderthenetworksusceptibletolargeclassofharmfulattacks[ 4 6 ].Ifthereplicasareplacedatwiselychosenlocations,theycanrevokelegitimatenodes,injectfalsedata,spyforcriticalinformationandmayevendisconnectthenetworkbyinvokingnoderevocationprotocolsthatarebasedonthresholdvotingschemes[ 7 10 13 17 ]. Themaintechnicalchallengeindetectingnodereplicationattacksarisesfromthememoryconstrainednatureofsensornodes.Ifasingleauthenticationkeyisbeingusedattwoormoredistinctlocationsinthenetwork,itmeanstherehasbeenreplication.Aneective 8

PAGE 9

1 ]forlow-endsensors,restrictstheamountofauthenticationinformationthatcanbestoredateachnode.Thelimitedbatterylifeofsensornodes,alsorestrictstheamountofenergythatcanbespentonreplicationdetection.Thusmemoryeciency,energyeciencyanddetectionprobabilityaretheprinciplecriteriafordetectionofreplicationattacks. Previousschemestodetectnodereplicationattackseitherincurhighmemoryoverheadorhighenergyoverheadorboth.Therstsolutionsforreplicationdetectionincludecentralizedschemes[ 13 ]andlocalizedvotingprotocols[ 7 ].However,theformerusuallyrelyonabasestation(BS)andhavetheproblemofsinglepointoffailure;thelattercannotdealwithdistributednodereplicationattacks,inwhichreplicasareplacedatleasttwohopsawayfromeachother.Afullydistributedsolutionisneededthatcandetectreplicasanywhereinthenetworkandyetincursmallmemoryandenergyoverhead. In[ 18 ],Parnoetal.proposedadistributeddetectionschemecalledLine-SelectedMulticast(LSM)whichutilizesnetworktopologytoselectwitnessnodesforanode'slocationandexploitsgeometricalpropertiesofthenetworktodetectreplicas.InLSM,foreachnodeinthenetworkadigitally-signedlocation-claimisgeneratedandstoredalongrandomlychosenklinesegments.Usuallyauniquekeyisusedtosigneachlocationwhichisstoredatthatlocation.Whenanodereplicaiscreateditusesthesamekeyasthenodetosignadierentlocationwhereitisplaced.Parnoetal.[ 18 ]showthatwhenkissucientlylarge,thelinesegmentsoftwoconictinglocationclaims(signedwiththesamekeyfordistinctlocations)intersectwithahighprobability.Thereplicationattackisdetectedattheintersectionnodesinceitcanseeboththeconictinglocationclaimsi.e.twolocationsforthesamenode.ThoughLSMprovidesadistributedsolution,ithasitsowndrawbacks.ItisshownthroughsimulationsinChapter 4 thatinanetworkofnnodes,inLSM,eachnodehastostoreO(kp 9

PAGE 10

14 ]toaccomplishouralgorithm.MELSeMreducesthenumberoflocationclaimsstoredateachsensornodetoO(k)fromO(kp Therestofthethesisisorganizedasfollows:backgroundandrelatedworkisdiscussedinChapter 2 .TheMELSeMprotocolispresentedinChapter 3 .InChapter 4 ,wepresentoursimulationresultsandcompareMELSeMwithLSM.Finally,inChapter 5 wediscussfutureworkdirectionsandwrapup. 10

PAGE 11

1 ].Duetotheprocessingcapabilityofthesensornodes,numerousapplicationsaredevelopedbasedonthem,especiallymilitaryapplications.Someotherexamplesoftheseapplicationsincludeburglaralarms,emergencyresponse,habitatmonitoring,battleeldsurveillance,homeautomationandtraccontrol.Thetypicalcharacteristicsoflowcostsensornodesincludenoshieldingorprotectionlayeringandlimitedbatterylife.Anotherimportantcharacteristicofsensornodesisthattheyareeasytodeploy.Theycanbeaddedtoanetworkwithoutadministrativesupervision.Duetothesecharacteristicstheyaresusceptibletoattacksfromanadversary. 11

PAGE 12

9 18 ]. 15 ]ispossibleinwhich,apacketcanberoutedhopbyhopuntilitreachesthedestination.Similarto[ 18 ]wealsoassumethatclocksofsensornodesarelooselysynchronized[ 12 ]andidentitybasedpublickeysystem[ 8 19 ]isused.Eachnode,byusingitsprivatekey,canestablishapairwisesecretwithitsneighboringnodesformutualauthenticationandalsocanproduceadigitalsignaturethatcanbeveriedbyothernodesinthenetwork. ThelocationclaimofanodeisrepresentedasC=hID;l;[H(ID;l)]K1i,whereIDisitsuniqueID,lisitslocation,HisahashfunctionandK1isitsprivatekey. AnodewhichstoresthecompletelocationclaimCfornodeandwhichcanverifytheidentityofiscalleditswitnessnode. 9 18 ]wemaketheassumptionthatanyclonednodehasatleastonelegitimateneighboringnode.Wealsoassumethatthenodesunderenemycontrolcancommunicateandcollaboratewithother 12

PAGE 13

13 ]scheme,eachnodesendsalistofitsneighborsandtheirlocationclaims,toacentralauthoritylikeaBaseStation.Thebasestationsearchesforthelistsofduplicateclaimsandndsexistingconicts.Thedisadvantageofthisapproachisthat,thereisasingleauthoritytoperformthischecking.Ifthisauthorityfailsoriscompromised,theentirenetworkcanbecompromised.Thusitsuersfromthewellknownsinglepointoffailureproblem.Theotherdisadvantageisthatsomeapplicationsmaynotusebasestationsatall.Furthermore,inthisapproachthenodesnearthebasestationsgetexhaustedsoonerandbecomeattacktargets. 7 ]scheme,neighborsofanodeusevotingprotocolsandcometoaconclusionabouttheauthenticityofanode.Theprincipaldrawbackofthisapproachisthatreplicationisaglobaleventandcannotbedetectedifdonejustlocally,whichmeansthatthismethodfailstodetectreplicasthataretwohopsawayfromthelocality. 9 18 23 ]requireeachnodeinthenetworktosignitsactuallocationwithitsprivatekeyinalocationclaim.Thecorrectnessofaclaimandthesignatureofthenodeisveriedbyitsneighboringnodes.Ifanoderefusestoprovideitslocationclaim,theneighborsofthenodecutitofromthenetworkanddenyanycommunicationwithit.Todetectreplicationattackswecheckifthesameprivatekeyisusedtosigntwoormorelocationclaims.Ifaprivatekeyhasbeenusedmorethanonceweconcludethatthenodewhichownstheprivate 13

PAGE 14

Inthedistributedsolutionsbasedonlocationclaimstheidentity-basedpublickeysystem[ 8 19 ]isusedinwhicheverynodestoresitsownprivatekeyandamasterpublickey.TheprivatekeyofanodeiscomputedusingitsnodeIDandamasterprivatekey.Thiscomputationisdonebeforethenodeisdeployedinthenetwork.Themasterprivatekeyiskeptsecretandnotloadedonanyofthenodes.Toverifythesignatureofalocationclaim,onlythepublickeyofthenodewhichproducestheclaimneedstobecomputed.ThepublickeyofanodecanbecomputedusingitsnodeIDandthemasterpublickey.Location-claimsaresaidtobeconictingwitheachotheriftheyhavesamenodeIDandsignaturebuttheirphysicallocationsaredierent.Asmentionedinsubsection 2.2.2 ,anadversaryhaslimitedcontrol,henceitwillnotbeabletoproduceavalidpairofnodeIDandprivatekeywhichcanpotentiallygenerateaveriablesignature,withouttheknowledgeofthemasterprivatekey.ThustheonlyoptionanadversaryhasistoproducereplicasbycopyingtheprivatekeysandnodeIDsofcompromisednodesontogenericnodes. Onebasicdierencebetweentheexistingdistributedsolutionsfordetectingnodereplicationattacksisthemannerinwhichtheystorelocationclaims,whichhasasignicantimpactonthememoryandcommunicationoverheadofthenetwork.Thesimplestsolutionistouseanetworkwidebroadcastprotocolwhereeachlocationclaimisbroadcasttoeverynodeinthenetworkandstoredateachofthem.Althoughthismethodcanachieve100%accuracyanddetectionprobability,itisnotfeasiblesinceitincurstremendousmemoryandcommunicationoverhead.Anothersolutionistostorethelocationclaimofanodeatapseudo-randomlocationinthenetworkthatisdeterminedbytheIDofthenode.Inthissolution,theconictingclaimswouldbeforwardedtothesamelocationforverication.The 14

PAGE 15

18 ] Tosolvethisproblemofpredictability,onesolutionistomakethelocationwhereaclaimwillbestoredunpredictable.Whichimplieswestoreeachclaimatrandomunpredictableplacesinthenetwork.Parnoet.al.in[ 18 ]usedthisapproachandproposedtwodistributedsolutionsnamelyRandomizeMulticast(RM)andLine-SelectedMulticast(LSM)thatstoreclaimsatrandomlocationsinthenetwork.InRM,eachlocationclaimisstoredatO(p 2.2.1 ),wherenisthenumberofnodesinthenetwork.Thebirthdayparadox[ 16 ]assuresthatacommonwitnessnodewillreceiveanytwoconictingclaimswithahighprobability.ThiswitnessnodewillbeabletodetectreplicationattacksinceitwouldhavetwoclaimswithsameIDsbutwithdierentlocations.Thewitnessnodewillthenbroadcasttheclaimtotheentirenetworkinformingthatthenodetowhichtheclaimbelongshasbeencompromised.ThecompromisednodeandallitsreplicaswiththesameIDarethencutofromthenetwork.TheproblemwithRMprotocolisthateachnodehastostoreO(p AlthoughLSMreducesthecommunicationoverheadinRM,itstillhasitsowndrawbacks.FirstproblemisthateachnodeinLSMisrequiredtostoreO(kp 15

PAGE 16

18 ].Moreover,sinceweknowthatthesecurityofadigitalsignaturedependsonitssize,thesizeofeachlocationclaimshouldbereasonablylargetoachievehigh-levelsecurity.Usually,thesizeofaclaimwouldbemorethan40bytessincedigitalsignaturerequires40bytesaccordingtoDSS[ 2 ].Thememoryofsensornodesislimitedanditisrequiredtoperformmanyotherfunctionssuchascommunication,measurementsandcomputations.ThismeansthatstoringO(kp 9 ].Thusanodeinthecentralregionofthenetworkareawouldhavetostoremuchmoreclaimsthanthenodesattheperiphery.Thisstoragerequirementcanbesohighforthenodesatthecenterthatmanyofthemmightcrashjustbecauseofmemoryoverow. Thegoalinthisthesisistoreducethememoryoverheadofthenetwork.WeuseanapproachsimilartoLSMwhiledevisinganoveldistributedprotocolusingecientBloomltersfordetectingnodereplicationattacks.InChapter 3 wedescribeourMELSeMprotocolindetail. 16

PAGE 17

WehaveusedBloomlterdatastructureinMELSeMprotocol.SowediscussBloomlterinthenextsectiontolaythefoundationforunderstandingMELSeM.WegivetheoutlineandbasicideabehindMELSeMinsection 3.2 anddescribeMELSeMprotocolindetailwithanexampleinsection 3.3 5 ]. TheBloomlterbeginsasanarrayofall0s.Thepurposeofthislteristosupportmembershipqueriesbyindicatingthepresenceorabsenceofanelementinaset.Wheneveranewitemzistobeaddedtoaset,itishashedutimesusingudierenthashfunctions,whereucanvaryasperneed.Eachoftheseuhashesresultinabitlocationinthearraywhichissetto1.Totestthemembershipofanelementz0intheset,similarhashingisrepeatedandthecorrespondingbitsarecheckedinthearray.Ifallthesebitshavebeensetto1,theelementmightbepresentintheset.Notethatwearesayingthattheelementmightbepresentinthesetbecausethecorrespondingbitsmightbesetto1asaresultofinsertionofotherelementsintheset.ThispropertyofBloomlterwhereitindicatesthatanelementispresentinthesetwhenitisactuallynotpresentiscalledfalsepositive.TheprobabilityoffalsepositivePBforaBloomlterasper[ 5 ]ism)u: wheresisthenumberofelementsintheset,uisthenumberofhashfunctionsusedandmisthenumberofbitsintheBloomlterarray.AnotherinterestingpropertyofBloomlteristhatithasnofalsenegativei.e.ifitrepresentsthatanelementisnotpresentintheset,thatmeansthattheelementisactuallynotpresentinthesetandis100%accurate. 17

PAGE 18

AsimpliedexampleofmappingofIDandlocationofanode'sclaimintwodistinctBloomltersstoredateachintermediatenode. 2.2.1 storethecompletelocationclaimswhileintermediatenodesarethenodeswhichstoreonlytheBloomlterrepresentations.ForstoringtheseBloomlterrepresentations,theintermediatenodesinsertanynode'sIDandlocationintoitstwoBloomlterswhichareknownastheIDlterandthelocationlterrespectively.Eventhoughtheintermediatenodesdonotstorethecompletecopyoftheclaim,theyhaveenoughinformationintheirtwoBloomlterstotellwhethertheyhaveseenaclaimpreviously.Figure 3-1 showsasimpliedexampleofmappinganode'sIDandlocationintwoBloomlters.TheupperBloomlterissimilartoIDlterandlowerBloomlterissimilartolocationlterusedinMELSeM. TheMELSeMprotocoloutlineisasfollows-Initially,thelocationclaimCofanodeismulticastedtoanumberofrandomly-selectedwitnessnodesinthenetworkviaitsone-hopneighborsusinggeographicrouting.Aone-hopneighborofhasacertainprobabilityptoparticipateinthemulticast.Ifitparticipates,itbecomesoneofthewitnessnodesforandcontinuestoforwardthelocationclaimCtoarandomly-selectedlocation.ThenodeclosesttothisrandomlyselectedlocationreceivesthelocationclaimCandbecomesanotherwitnessnodewbystoringC.AlltheintermediatenodesontheroutingpathPfromtowstorejusttheBloomlterrepresentationsofIDandlintheirIDltersandlocation 18

PAGE 19

OntheforwardingpathfromtoldestwheneveranynodereceivesC,atwo-phaseconictcheckisperformedbyittotestifanyconictingclaimscanbedetected.Intherstphase,thesignaturepresentinCisveriedby.Ifthelocationclaimappearstobeinvalidorfake,itisdroppedimmediately.Thenbeingawitnessnodeforothernodesinthenetwork,itcomparesCwiththelocationclaimsstoredlocallywithit.IfndsaclaimconictingwithC,areplicationattackisdetected.Ittheninvokesarevocationprotocol,inwhichtheconictingclaimsareforwardedtothewitnessnodes.Therstwitnessnodetoreceivethisinformationbroadcaststheconictingclaimstotheentirenetworksothatthereplicationattackcanbetakencareof.Ifnodedoesnotdetectanyconictingclaims,theMELSeMprotocolproceedstophasetwo. Inphasetwo,therearetwopossiblecases.TherstcaseiswherenodendsmappingforIDinitsIDlterwhilethereisnomappingforlinitslocationlter.Thismeans 19

PAGE 20

AnexampleofreplicadetectionprocessusedinMELSeM thatthereareconictingrepresentationsinthelters.ItimpliesthataconictingclaimC0withthesameIDandadierentlocationl0hasgoneviaandleftitstrailinthelters. Inthesecondcaseofphasetwo,whenthenodedoesnotndIDinitsIDlterorlinitslocationlter,thenewlyarrivedclaimCistreatedasalegitimateclaim.Thisclaimisapprovedofhavingpassedthetwo-phaseconictcheck.ThentriestoforwardCtowardsldest.Iftherearenoothernodesclosetoldest,storesthelocationclaiminitsmemoryandactsasawitnessnodeitself.Elseifthereisanothernodeclosertoldest,forwardstheclaimtothatnodeandactsasanintermediatenodebymakingappropriateentriesinitstwoBloomltersforIDandl.ThisexplainstheworkingofMELSeMprotocol. Figure 3-2 showsanexampleofhowreplica'saredetectedinMELSeM.Alocationclaimforwardingpathisshownusingsolidarrowswhilethedashedarrowsshowtheactiontakenbyanintermediatenodeuponreplicadetection.NodeinitiatesarevocationprotocolbylocallybroadcastingCandC0toitsone-hopneighbors.Amongtheseneighbors,theonesthatndconictswithCorC0intheirBloomlterscontinuethisone-hoplocalbroadcastuntiltheclaimeventuallyreachesoneofthewitnessnodes,whichthennotiestheentirenetworkaboutthereplicationattack. Itisworthnotingthatduringtherevocation,notjustthereplicanodebutalsothelegitimatenodethathasbeencompromisedisremoved.Thereasonforthisisthat,acompromisednodecanbeduplicatedanynumberoftimesandwewanttopreventit. 20

PAGE 21

m)u(1(1esu m)u)(3{2) Theprobabilityoffalseinitiationofrevocationcanbemadenegligiblebyadjustingvariousparameters.ForexamplebyincreasingthenumberofbitsmintheBloomlterarrayorbyincreasingthenumberofhashfunctionsuusedetc.WehavefoundthroughextensivesimulationsthatthisprobabilityissosmallthatithardlyevercausestheMELSeMprotocoltofalselyreportareplicationattack. 21

PAGE 22

18 ]weassumebidirectionalcommunicationmodelwithlinksbetweenone-hopneighbors.Weselecttransmissionrangesuchthateachnodehasapproximately20neighbors.Locationclaimsareforwardedbysimulatingasimpliedgeographicroutingprotocol[ 15 ]whereanodegreedilyforwardsalocationclaimtotheneighborclosesttothedestination.Whenanodendsnonodeclosertothedestination,forwardingisstopped.WeusesixlinesegmentsforLSM(asorginallyusedin[ 18 ])aswellasforMELSeM.Wecompromisearandomnodeinthenetworkandinsertitsonereplicaatarandomlocationinthenetwork.Wealsoconsiderthecasesformultiplereplicas.Wecreate100randomnetworkgraphsforeachnetworksizeandcalculatetheaverageforresults. Foroursimulationswehaveassumedthatthesizeofeachlocationclaimis46byteswhere2bytesareforID,4bytesforlocation(xandycoordinates)and40bytesforthedigitalsignatureasperDSS[ 2 ].WeadjustthenumberofbitsmandnumberofmembersstobeinsertedintheBloomltersuchthatm/s=15.Thismeansweuse15bits;alongwith7hashfunctionsutostoreanelementinaBloomlter. Figure 4-1 showsasquaredeploymentareawhichisdividedinto20equalsub-areas.Eachofthesesub-areasaccountsfor5%ofthedeploymentregion.Thenumberingofsub-areasisdonefrom0through19,withthecentralsub-areabeingnumber0andtheoutermostsub-areabeing19. 1 ],soitisacriticalresource.Asensornodehastoperformvariousfunctionsinanetworkssuchascommunication,measurements,collaboration,computationsetc.Thusthememorythatmightbeavailable 22

PAGE 23

Anexampleof5%incrementalsubareas forsecuritypurposesincludingreplicationdetectioncanbeverylimited.ThebasicdierencebetweenLSMandMELSeMisthatinLSMeverynodeinthenetworkstoresacompletecopyofthelocationclaimwhereasinMELSeMonlywitnessnodesstorethecompletecopyoflocationclaim.TherestofthenodesinMELSeMstoreonlytheBloomlterrepresentationsofthelocationclaimsthatpassthroughthem.StoringseveralhundredsofcompletelocationclaimswillleadtomemoryoverowofnodesinLSM,whichmightcausethenodestocrash.TheecientuseofBloomltersinourprotocoldrasticallyreducesthememoryrequirementofthenetwork,preventingthenodesfrommemoryoverowsandcrashing. Figure 4-2 showsaveragememoryconsumptionbyLSMandMELSeMfordierentnetworksizes.Asitcanbeobserved,thememoryconsumptionincreasesasnetworksizeincreases.ThisisbecauseeachnodeinthenetworkhastostoremoreinformationeitherintheformoflocationclaimsorBloomlterrepresentations.MELSeMreducesthememoryoverheadofthenetworkbynearly70%comparedtoLSM. Figure 4-3 showsthemaximalmemoryconsumptionforLSMandMELSeM.Maximalmemoryconsumptionisthemaximumamountofmemoryconsumedbyanodeinthe 23

PAGE 24

Averagememoryconsumption Figure4-3. Maximalmemoryconsumption 24

PAGE 25

Averagememorydistribution network.ItisclearfromthegurethatformostnetworksizesthemaximalmemoryconsumptionofLSMexceeds10KBwhichcaneasilyexhaustthememorycapacityoflow-endsensornodes.Inthisscenario,MELSeMstandsout,whichcanreducethememoryoverheadofthenetworkupto85%. Figure 4-4 showsthedistributionofmemoryconsumptionforLSMandMELSeMinanetworkof5000nodes.Asdescribedinsimulationsettingsatthebeginningofthischapter,thenetworkisdividedinto20equalsub-areasasshownin 4-1 .ItisquiteclearthatLSMconsumesalotmorememoryinthecentralsub-areasthanintheoutersub-areas.Ontheotherhand,MELSeMbalancesthememoryconsumptionacrossallsub-areasevenly.ThisisbecauseLSMhasmorewitnessnodes(storingcompletecopiesoflocationclaims)concentratedinthecentralregionofthenetworkwhereasinMELSeMwitnessnodesareevenlyspreadacrossthenetwork.MELSeMalsohasmanyintermediatenodesconcentratedinthecentralregion,howevertheircumulativememoryconsumptionisnottoohighbecausetheyusespaceecientBloomltersinsteadofstoringthecompleteclaims. 25

PAGE 26

21 ]inwhichanewnodehasatotalof324,000mJofavailableenergy.Bitsendingcosts0.059mJandbitreceivingcosts0.028mJ.Fromoursimulationswefoundthat,MELSeMincursapproximately3%moreenergyconsumptiononaveragethanLSM.ThisisbecauseinLSM,anyintermediatenodewhichdetectsreplicationcanitselfbroadcastboththeconictingclaimstothenetworksinceithasthecompletecopyoflocationclaim.WhereasinMELSeM,extracommunicationisrequiredfortherevocationprotocolinwhich,theintermediatenodesendstheconictingclaimstothewitnessnodessincetheintermediatenodeitselfdoesn'thavethecompletecopyoflocationclaim.ThoughtheenergyconsumptionofMELSeMisslightlyhigherthanLSM,itisquiteacceptableconsideringtheamountofmemorysavingsachievedthroughMELSeM. Intermsofcommunication,sinceweassumethatthelengthofonelinesegmentisO(p 4-5 showsthedetectionprobabilityofLSMandMELSeMforauniformlydeployedsquareareawithnodedensitiesrangingfrom1000through10000.ItisclearfromthegurethatPdrangesfrom85%to95%forbothLSMandMELSeMandisapproximatelysameforbothprotocols.Onemightthinkthatincreasingthenumberofreplicasinthenetworkisharmful,butitactuallyimprovesthedetectionprobabilitysincetherewillmoreline-segmentsinthenetworkwhichincreasethechancesofconictingclaimsbeingcaught. 26

PAGE 27

DetectionProbabilityofLSMandMELSeMinauniformlydeployedsquareareawithdierentnodedensities 27

PAGE 28

InLSMaswellasinMELSeM,krandomlinesegmentsareusedtostorelocation-claiminformation.Whenlinesegmentsaredrawnatrandominaconvexarea,theypassthroughthecentralregionmorefrequentlythanotherregions.Sothenetworkwillhavehighernodedensityinthecentralregionofthenetwork.Thesenodesinthecentralregionofthenetworkincurhighermemoryandenergyoverheadthanthenodesattheperiphery.Thisiscalledthecrowdedcenterproblem.Contietal.[ 9 ]solvethecrowdedcenterbytheuseofaperiodicallyrenewedpseudorandomnumberonanetworkwidescale.However,theinfrastructurerequiredforthissolutionmaynotbealwaysavailableinthenetwork.Toreducetheenergyandmemoryburdenonthenodesinthecentralregionofthenetwork,withoutrelyingonadditionalinfrastructurecanbeaninterestingareaforfutureresearch. Theotherproblemwithlinesegmentsmethodisthat,whentwolinesegmentsintersecttheymightnotintersectatanode.Whensuchanintersectionhappens,nocommonnodehassucientinformationtodetectnodereplicationattack.Furtherresearchtohandlesuchoccurrencescanbedone. ThenodereplicationdetectionprobabilityofMELSeMissimilartothatofLSMandisintherangeof85-95%.Althoughthisamountofaccuracyisnotbad,itisdesirabletoattainadetectionprobabilityandaccuracyof100%inthefuture. 28

PAGE 29

29

PAGE 30

[1] Sensornode.[internet]Wikipedia;[updated2009June29;cited2009June30].Availabefrom: http://en.wikipedia.org/wiki/Sensor node [2] Digitalsignaturestandard.FIPSPUB186-3,March,2006. [3] I.F.Akyildiz,W.Su,Y.Sankarasubramaniam,andE.Cayirci.Wirelesssensornetworks:asurvey.InternationalJournalofComputerandTelecommunicationsNet-workingElsevier,38(4):393-422,Mar.2002. [4] A.Becher,Z.Benenson,andM.Dornseif.Tamperingwithmotes:real-worldphysicalattacksonwirelesssensornetworks.InProceedingsofthe3rdInternationalConferenceonSecurityinPervasiveComputing(SPC),pages104-118,2006. [5] A.BroderandM.Mitzenmacher.Networkingapplicationsofbloomlters:Asurvey.InProceedingsofAllertonConference,2002. [6] S.CapkunandJ.P.Hubaux.Securepositioningofwirelessdeviceswithapplicationtosensornetworks.InINFOCOM,pages1917-1928,2005. [7] H.Chan,A.Perrig,andV.D.Song.Randomkeypredistributionschemesforsensornetworks.InProc.ofIEEESymposiumonSecurityandPrivacy(S&P'03),May.2003. [8] C.Cocks.Anidentitybasedencryptionschemebasedonquadraticresides.InPro-ceedingsofthe8thIMAInternationalConferenceonCryptographyandCoding,pages360-363,London,UK,Springer-Verlag.,2001. [9] M.Conti,R.D.Pietro,andL.V.Mancini.Arandomized,ecient,anddistributedprotocolforthedetectionofnodereplicationattacksinwirelesssensornetworks.InProc.ofthe8thACMInternationalSymposiumonMobileAdHocNetworkingandComput-ing(MobiHoc'07),pages80-89,2007. [10] J.R.Douceur.Thesybilattack.InProceedingsofWorkshoponPeer-to-PeerSystems(IPTPS).,Mar.2002. [11] J.Dyer,M.Lindemann,R.Sailer,L.VanDoorn,S.W.Smith,andS.Weingart.Buildingtheibm4758securecoprocessor.IEEEComputer,2001. [12] J.Elson,L.Girod,andD.Estrin.Fine-grainednetworktimesynchronizationusingreferencebroadcasts.SIGOPSOperatingSystemsReview,36(SI):147-163,2002. [13] L.EschenauerandV.Gligor.Akey-managementschemefordistributedsensornetworks.InProc.oftheACMConferenceonComputerandCommunicationSecu-rity(CCS),Nov.2002. [14] V.D.Gligor.Securityofemergentpropertiesinad-hocnetworks.InProc.ofInterna-tionalWorkshoponSecurityProtocols,Apr.2004. 30

PAGE 31

B.KarpandH.T.Kung.Gpsr:Greedyperimeterstatelessroutingforwirelessnetworks.InProceedingsofthe6thAnnualACM/IEEEInternationalConferenceonMobileCom-putingandNetworking(MobiCom'00),pages243-254,2000. [16] A.J.Menezes,S.A.Vanstone,andP.C.V.Orschot.Handbookofappliedcryptography.CRCPress,Inc.,1996. [17] J.Newsome,E.Shi,D.Song,andA.Perrig.Thesybilattackinsensornetworks:Analysisanddefenses.InProceedingsofIEEEConferenceonInformationProcessinginSensorNetworks(IPSN).,Apr.2004. [18] B.Parno,A.Perrig,andV.D.Gligor.Distributeddetectionofnodereplicationattacksinsensornetworks.InProc.of2005IEEESymposiumonSecurityandPrivacy(S&P'05),pages49-63,Washington,DC,USA,2005. [19] A.Shamir.Identity-basedcryptosystemsandsignatureschemes.InProceedingsofCRYPTO84onAdvancesincryptology,pages47-53,Springer-VerlagNewYork,Inc.,1985. [20] S.W.SmithandS.Weingart.Buildingahighperformance,programmablesecurecoprocessor.ComputerNetworks,SpecialIssueonComputerNetworkSecurity.,Apr.1999. [21] A.Wander,N.Gura,H.Eberle,V.Gupta,andS.C.Shantz.Energyanalysisofpublic-keycryptographyforwirelesssensornetworks.InProceedingsoftheThirdAnnualIEEEInternationalConferenceonPervasiveComputingandCommunications(PER-COM'05),pages324-328,2005. [22] S.WeingartandS.Weingart.Physicalsecuritydevicesforcomputersubsystems:Asurveyofattacksanddefenses.InCryptographicHardwareandEmbeddedSystems(CHES).,Aug.2000. [23] BoZhu,VenkataGopalaKrishnaAddada,SanjeevSetia,SushilJajodia,andSankardasRoy.Ecientdistributeddetectionofnodereplicationattacksinsensornetworks.ComputerSecurityApplicationsConference,volume0,pages257-267,issn1063-9527,LosAlamitos,CA,USA,2007. 31

PAGE 32

VishalisfromthecityofLatur,whichisinMaharashtrastateofIndia.Hedidhisbachelor'sincomputerscienceandengineeringfromGovernmentCollegeofEngineering{Aurangabad(2005).HeworkedwithCognizantTechnologySolutionsPvt.LtdinPuneasaProgrammerAnalystforoneandahalfyears.Hedidhismaster'sincomputerengineeringfromtheComputerandInformationScienceandEngineeringDepartmentattheUniversityofFlorida(2009).HisresearchinterestsincludeNetwork&SystemsSecurity,QualityofServiceinWirelessNetworks,Internetprotocols,DistributedSystemsandSecureEmbeddedSystems. 32