Distributed Detection in the Presence of Byzantine Attacks
Distributed detection in the presence of cooperative (Byzantine) attack is considered. It is assumed that a fraction of the monitoring sensors are compromised by an adversary, and these compromised (Byzantine) sensors are reprogrammed to transmit fictitious observations aimed at confusing the decisi...
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| Veröffentlicht in: | IEEE transactions on signal processing 2009-01, Vol.57 (1), p.16-29 |
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| container_title | IEEE transactions on signal processing |
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| creator | Marano, S. Matta, V. Lang Tong |
| description | Distributed detection in the presence of cooperative (Byzantine) attack is considered. It is assumed that a fraction of the monitoring sensors are compromised by an adversary, and these compromised (Byzantine) sensors are reprogrammed to transmit fictitious observations aimed at confusing the decision maker at the fusion center. For detection under binary hypotheses with quantized sensor observations, the optimal attacking distributions for Byzantine sensors that minimize the detection error exponent are obtained using a ldquowater-fillingrdquo procedure. The smallest error exponent, as a function of the Byzantine sensor population, characterizes the power of attack. Also obtained is the minimum fraction of Byzantine sensors that destroys the consistency of detection at the fusion center. The case when multiple measurements are made at the remote nodes is also considered, and it is shown that the detection performance scales with the number of sensors differently from the number of observations at each sensor. |
| doi_str_mv | 10.1109/TSP.2008.2007335 |
| format | Article |
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It is assumed that a fraction of the monitoring sensors are compromised by an adversary, and these compromised (Byzantine) sensors are reprogrammed to transmit fictitious observations aimed at confusing the decision maker at the fusion center. For detection under binary hypotheses with quantized sensor observations, the optimal attacking distributions for Byzantine sensors that minimize the detection error exponent are obtained using a ldquowater-fillingrdquo procedure. The smallest error exponent, as a function of the Byzantine sensor population, characterizes the power of attack. Also obtained is the minimum fraction of Byzantine sensors that destroys the consistency of detection at the fusion center. The case when multiple measurements are made at the remote nodes is also considered, and it is shown that the detection performance scales with the number of sensors differently from the number of observations at each sensor.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2008.2007335</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Batteries ; Byzantine attack ; Consistency ; Costs ; Cryptography ; Data mining ; Decision making ; distributed detection ; Error detection ; Exact sciences and technology ; Exponents ; Information, signal and communications theory ; Jamming ; Miscellaneous ; Monitoring ; network defense ; Optimization ; Sensor fusion ; Sensor phenomena and characterization ; Sensors ; Signal design ; Signal processing ; Telecommunications and information theory ; Wireless sensor networks</subject><ispartof>IEEE transactions on signal processing, 2009-01, Vol.57 (1), p.16-29</ispartof><rights>2009 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c492t-2a056f17c4e83c7b26542daf252c0eda2f10666abe3107d03cc946aa8f465c23</citedby><cites>FETCH-LOGICAL-c492t-2a056f17c4e83c7b26542daf252c0eda2f10666abe3107d03cc946aa8f465c23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4663901$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,4010,27900,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4663901$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21020778$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Marano, S.</creatorcontrib><creatorcontrib>Matta, V.</creatorcontrib><creatorcontrib>Lang Tong</creatorcontrib><title>Distributed Detection in the Presence of Byzantine Attacks</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>Distributed detection in the presence of cooperative (Byzantine) attack is considered. It is assumed that a fraction of the monitoring sensors are compromised by an adversary, and these compromised (Byzantine) sensors are reprogrammed to transmit fictitious observations aimed at confusing the decision maker at the fusion center. For detection under binary hypotheses with quantized sensor observations, the optimal attacking distributions for Byzantine sensors that minimize the detection error exponent are obtained using a ldquowater-fillingrdquo procedure. The smallest error exponent, as a function of the Byzantine sensor population, characterizes the power of attack. Also obtained is the minimum fraction of Byzantine sensors that destroys the consistency of detection at the fusion center. The case when multiple measurements are made at the remote nodes is also considered, and it is shown that the detection performance scales with the number of sensors differently from the number of observations at each sensor.</description><subject>Applied sciences</subject><subject>Batteries</subject><subject>Byzantine attack</subject><subject>Consistency</subject><subject>Costs</subject><subject>Cryptography</subject><subject>Data mining</subject><subject>Decision making</subject><subject>distributed detection</subject><subject>Error detection</subject><subject>Exact sciences and technology</subject><subject>Exponents</subject><subject>Information, signal and communications theory</subject><subject>Jamming</subject><subject>Miscellaneous</subject><subject>Monitoring</subject><subject>network defense</subject><subject>Optimization</subject><subject>Sensor fusion</subject><subject>Sensor phenomena and characterization</subject><subject>Sensors</subject><subject>Signal design</subject><subject>Signal processing</subject><subject>Telecommunications and information theory</subject><subject>Wireless sensor networks</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kL1LA0EQxRdRMEZ7weYQFJuLs993djF-gqBgCrtls5nDjcmd7m6K-Ne7IcHCwmZmYH7vMfMIOaYwoBTqy_Hry4ABVOuiOZc7pEdrQUsQWu3mGSQvZaXf9slBjDMAKkSteuTqxscU_GSZcFrcYEKXfNcWvi3SOxYvASO2DouuKa5X37ZNvsVimJJ1H_GQ7DV2HvFo2_tkfHc7Hj2UT8_3j6PhU-lEzVLJLEjVUO0EVtzpCVNSsKltmGQOcGpZQ0EpZSfIKegpcOdqoaytGqGkY7xPzje2n6H7WmJMZuGjw_ncttgto-GiUrSSMoMX_4JUacpqXXGV0dM_6KxbhjZ_YbIZ5YxRyBBsIBe6GAM25jP4hQ0rQ8GsMzc5c7PO3Gwzz5Kzra-Nzs6bYFvn468uuzLQ-YI-OdlwHhF_10IpXgPlP7sRh_k</recordid><startdate>200901</startdate><enddate>200901</enddate><creator>Marano, S.</creator><creator>Matta, V.</creator><creator>Lang Tong</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>200901</creationdate><title>Distributed Detection in the Presence of Byzantine Attacks</title><author>Marano, S. ; Matta, V. ; Lang Tong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c492t-2a056f17c4e83c7b26542daf252c0eda2f10666abe3107d03cc946aa8f465c23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Applied sciences</topic><topic>Batteries</topic><topic>Byzantine attack</topic><topic>Consistency</topic><topic>Costs</topic><topic>Cryptography</topic><topic>Data mining</topic><topic>Decision making</topic><topic>distributed detection</topic><topic>Error detection</topic><topic>Exact sciences and technology</topic><topic>Exponents</topic><topic>Information, signal and communications theory</topic><topic>Jamming</topic><topic>Miscellaneous</topic><topic>Monitoring</topic><topic>network defense</topic><topic>Optimization</topic><topic>Sensor fusion</topic><topic>Sensor phenomena and characterization</topic><topic>Sensors</topic><topic>Signal design</topic><topic>Signal processing</topic><topic>Telecommunications and information theory</topic><topic>Wireless sensor networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Marano, S.</creatorcontrib><creatorcontrib>Matta, V.</creatorcontrib><creatorcontrib>Lang Tong</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Marano, S.</au><au>Matta, V.</au><au>Lang Tong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Distributed Detection in the Presence of Byzantine Attacks</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>2009-01</date><risdate>2009</risdate><volume>57</volume><issue>1</issue><spage>16</spage><epage>29</epage><pages>16-29</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>Distributed detection in the presence of cooperative (Byzantine) attack is considered. It is assumed that a fraction of the monitoring sensors are compromised by an adversary, and these compromised (Byzantine) sensors are reprogrammed to transmit fictitious observations aimed at confusing the decision maker at the fusion center. For detection under binary hypotheses with quantized sensor observations, the optimal attacking distributions for Byzantine sensors that minimize the detection error exponent are obtained using a ldquowater-fillingrdquo procedure. The smallest error exponent, as a function of the Byzantine sensor population, characterizes the power of attack. Also obtained is the minimum fraction of Byzantine sensors that destroys the consistency of detection at the fusion center. The case when multiple measurements are made at the remote nodes is also considered, and it is shown that the detection performance scales with the number of sensors differently from the number of observations at each sensor.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TSP.2008.2007335</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | IEEE transactions on signal processing, 2009-01, Vol.57 (1), p.16-29 |
| issn | 1053-587X 1941-0476 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Batteries Byzantine attack Consistency Costs Cryptography Data mining Decision making distributed detection Error detection Exact sciences and technology Exponents Information, signal and communications theory Jamming Miscellaneous Monitoring network defense Optimization Sensor fusion Sensor phenomena and characterization Sensors Signal design Signal processing Telecommunications and information theory Wireless sensor networks |
| title | Distributed Detection in the Presence of Byzantine Attacks |
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