Malicious data deception attacks against power systems: A new case and its detection method

Dajun Du, Rui Chen, Xue Li, Lei Wu, Peng Zhou, Minrui Fei

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

Power systems usually employ bad data detection (BDD) to avoid faulty measurements caused by their anomalies, and hence can ensure the security of the state estimation of power systems. However, recently BDD has been found vulnerable to malicious data deception attacks submerged in big data. Such attacks can purposely craft sparse measurement values (i.e. attack vectors) to mislead power estimates, while not posing any anomalies to the BDD. Some related work has been proposed to emphasize this attack. In this paper, a new malicious data deception attack by considering a practical attacking situation is investigated, where the attacker has limited resources for corrupting measurements. In this case, attackers generate attack vectors with less sparsity to evade conventional BDD, while using a convex optimization method to balance the sparsity and magnitude of attack vectors. Accordingly, the effects of such an attack on operational costs and the risks of power systems are analysed in detail. Moreover, according to security evaluation for individual measurements, such attacks can be detected with high probability by just securing one critical measurement. Numerical simulations illustrate the effectiveness of the proposed new attack case and its detection method.

Original languageEnglish
Pages (from-to)1590-1599
Number of pages10
JournalTransactions of the Institute of Measurement and Control
Volume41
Issue number6
DOIs
StatePublished - 1 Apr 2019

Keywords

  • Smart grid
  • bad data detection (BDD)
  • detection strategy
  • line overload risk
  • malicious data deception attack
  • optimal power flow

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