Smarter security in the smart grid
A new formulation for detection of false data injection attacks in the smart grid is introduced. The attack detection problem is posed as a statistical learning problem in which the observed measurements are classified as being either attacked or secure. The proposed approach provides an attack dete...
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| creator | Ozay, M. Esnaola, I. Yarman Vural, Fatos T. Kulkarni, S. R. Poor, H. Vincent |
| description | A new formulation for detection of false data injection attacks in the smart grid is introduced. The attack detection problem is posed as a statistical learning problem in which the observed measurements are classified as being either attacked or secure. The proposed approach provides an attack detection framework that surmounts over the constraints arising due to the sparse structure of the problem and implicitly exploits any available prior knowledge about the system. Specifically, three supervised learning algorithms are presented. These procedures operate by first observing the power system in order to construct a training dataset which is later used to detect the attacks in new observations. In order to assess the validity of the proposed techniques, the behavior of the proposed algorithms is examined on IEEE test systems. |
| doi_str_mv | 10.1109/SmartGridComm.2012.6486002 |
| format | Conference Proceeding |
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In order to assess the validity of the proposed techniques, the behavior of the proposed algorithms is examined on IEEE test systems.</description><subject>attack detection</subject><subject>classification</subject><subject>convex optimization</subject><subject>Estimation</subject><subject>Jacobian matrices</subject><subject>Kernel</subject><subject>Logistics</subject><subject>machine learning</subject><subject>Smart grid security</subject><subject>Statistical learning</subject><subject>Support vector machines</subject><subject>Vectors</subject><isbn>9781467309103</isbn><isbn>1467309109</isbn><isbn>1467309095</isbn><isbn>9781467309097</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjz9PwzAUxI0QElDyCVis7gnv-fnviCJokSox0M6VqW0wIoDsMPTbEyDT6W743R1jS4QOEdzN0-DLuCo59J_D0AlA0WlpNYA4YZcotSFw4NQpa5yxs0egc9bU-gYAiEKSoQu2_CPFwms8fJc8Hnn-4ONr5PU35y9TxRU7S_69xmbWBdvd3237dbt5XD30t5s2o1Fji95P2CCtD144MiCeEQ2RsuSNTzoo67yIIjkpkzLaR2HIkgpow8EkoAW7_ufmGOP-q-RpwXE_36Ifji9CVQ</recordid><startdate>201211</startdate><enddate>201211</enddate><creator>Ozay, M.</creator><creator>Esnaola, I.</creator><creator>Yarman Vural, Fatos T.</creator><creator>Kulkarni, S. 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Vincent</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ozay, M.</au><au>Esnaola, I.</au><au>Yarman Vural, Fatos T.</au><au>Kulkarni, S. R.</au><au>Poor, H. Vincent</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Smarter security in the smart grid</atitle><btitle>2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)</btitle><stitle>SmartGridComm</stitle><date>2012-11</date><risdate>2012</risdate><spage>312</spage><epage>317</epage><pages>312-317</pages><isbn>9781467309103</isbn><isbn>1467309109</isbn><eisbn>1467309095</eisbn><eisbn>9781467309097</eisbn><abstract>A new formulation for detection of false data injection attacks in the smart grid is introduced. The attack detection problem is posed as a statistical learning problem in which the observed measurements are classified as being either attacked or secure. The proposed approach provides an attack detection framework that surmounts over the constraints arising due to the sparse structure of the problem and implicitly exploits any available prior knowledge about the system. 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| ispartof | 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm), 2012, p.312-317 |
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| language | eng |
| recordid | cdi_ieee_primary_6486002 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | attack detection classification convex optimization Estimation Jacobian matrices Kernel Logistics machine learning Smart grid security Statistical learning Support vector machines Vectors |
| title | Smarter security in the smart grid |
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