Smarter security in the smart grid

Mete Ozay, Inaki Esnaola, Fatos T. Yarman Vural, Sanjeev R. Kulkarni, H. Vincent Poor

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012
ページ312-317
ページ数6
DOI
出版ステータスPublished - 2012 12 1
イベント2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012 - Tainan, Taiwan, Province of China
継続期間: 2012 11 52012 11 8

出版物シリーズ

名前2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012

Other

Other2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012
CountryTaiwan, Province of China
CityTainan
Period12/11/512/11/8

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Communication

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