REACT to Cyber Attacks on Power Grids

Motivated by the recent cyber attack on the Ukrainian power grid, we study cyber attacks on power grids that affect both the physical infrastructure and the data at the control center-which therefore are cyber-physical in nature. In particular, we assume that an adversary attacks an area by: (i) rem...

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Published inIEEE transactions on network science and engineering Vol. 6; no. 3; pp. 459 - 473
Main Authors Soltan, Saleh, Yannakakis, Mihalis, Zussman, Gil
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN2327-4697
2334-329X
DOI10.1109/TNSE.2018.2837894

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Summary:Motivated by the recent cyber attack on the Ukrainian power grid, we study cyber attacks on power grids that affect both the physical infrastructure and the data at the control center-which therefore are cyber-physical in nature. In particular, we assume that an adversary attacks an area by: (i) remotely disconnecting some lines within the attacked area, and (ii) modifying the information received from the attacked area to mask the line failures and hide the attacked area from the control center. For the latter, we consider two types of attacks: (i) data distortion: which distorts the data by adding powerful noise to the actual data, and (ii) data replay: which replays a locally consistent old data instead of the actual data. We use the DC power flow model and prove that the problem of finding the set of line failures given the phase angles of the nodes outside of the attacked area is strongly NP-hard, even when the attacked area is known. However, we introduce the polynomial time REcurrent Attack Containment and deTection (REACT) Algorithm to approximately detect the attacked area and line failures after a cyber-physical attack. We numerically show that it performs well in detecting the attacked area, and detecting single, double, and triple line failures in small and large attacked areas.
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ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2018.2837894