Show simple item record

dc.contributor.authorLiao, Weixian
dc.contributor.authorSalinas Monroy, Sergio A.
dc.contributor.authorLi, Mingyang
dc.contributor.authorLi, Pan
dc.contributor.authorLoparo, Kenneth A.
dc.identifier.citationLiao, Weixian; Salinas Monroy, Sergio A.; Li, Ming; Li, Pan; Loparo, Kenneth A. 2017. Cascading failure attacks in the power system: a stochastic game perspective. IEEE Internet of Things Journal, vol. 4:no. 6:pp 2247-2259en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractElectric power systems are critical infrastructure and are vulnerable to contingencies including natural disasters, system errors, malicious attacks, etc. These contingencies can affect the world's economy and cause great inconvenience to our daily lives. Therefore, security of power systems has received enormous attention for decades. Recently, the development of the Internet of Things (IoT) enables power systems to support various network functions throughout the generation, transmission, distribution, and consumption of energy with IoT devices (such as sensors, smart meters, etc.). On the other hand, it also incurs many more security threats. Cascading failures, one of the most serious problems in power systems, can result in catastrophic impacts such as massive blackouts. More importantly, it can be taken advantage by malicious attackers to launch physical or cyber attacks on the power system. In this paper, we propose and investigate cascading failure attacks (CFAs) from a stochastic game perspective. In particular, we formulate a zero-sum stochastic attack/defense game for CFAs while considering the attack/defense costs, budget constraints, diverse load shedding costs, and dynamic states in the system. Then, we develop a Q-CFA learning algorithm that works efficiently in power systems without any a priori information. We also formally prove that the convergence of the proposed algorithm achieves a Nash equilibrium. Simulation results validate the efficacy and efficiency of the proposed scheme by comparisons with other state-of-the-art approaches.en_US
dc.description.sponsorshipU.S. National Science Foundation under Grant CNS-1602172 and Grant CNS-1566479. The work of M. Li was supported by the U.S. National Science Foundation under Grant CNS-1566634 and Grant ECCS-1711991.en_US
dc.relation.ispartofseriesIEEE Internet of Things Journal;v.4:no.6
dc.subjectCascading failure attacks (CFAs)en_US
dc.subjectNash equilibriumen_US
dc.subjectQ-CFA learning algorithmen_US
dc.subjectStochastic gamesen_US
dc.titleCascading failure attacks in the power system: a stochastic game perspectiveen_US
dc.rights.holderCopyright © 2017, IEEEen_US

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record