Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids

Jinping Hao, Robert Piechocki, Dritan Kaleshi, Woon Hau Chin, Zhong Fan

Research output: Contribution to journalArticle (Academic Journal)peer-review

125 Citations (Scopus)


This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. First, methods of constructing sparse stealth attacks are developed for two typical scenarios: 1) random attacks in which arbitrary measurements can be compromised; and 2) targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper, it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method, but without the combinatorial complexity. Third, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements, as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems.
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Industrial Informatics
Issue number5
Early online date1 Sep 2015
Publication statusPublished - 1 Oct 2015

Structured keywords

  • Digital Health


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