An Adaptive Markov Strategy for Defending Smart Grid False Data Injection From Malicious Attackers

Authored by Jianye Hao, Eunsuk Kang, Jun Sun, Zan Wang, Zhaopeng Meng, Xiaohong Li, Zhong Ming

Date Published: 2018

DOI: 10.1109/tsg.2016.2610582

Sponsors: Chinese National Natural Science Foundation

Platforms: No platforms listed

Model Documentation: Other Narrative Pseudocode Mathematical description

Model Code URLs: Model code not found

Abstract

We present a novel defending strategy, adaptive Markov strategy (AMS), to protect a smart-grid system from being attacked by unknown attackers with unpredictable and dynamic behaviors. One significant merit of deploying AMS to defend the system is that it is theoretically guaranteed to converge to a best response strategy against any stationary attacker, and converge to a Nash equilibrium (NE) in case of self-play (the attacker is intelligent enough to use AMS to attack). The effectiveness of AMS is evaluated by considering the class of the data integrity attacks in which an attacker manages to inject false voltage information into the intelligent voltage controller in a substation. This kind of attack may cause load shedding and potentially a blackout. We perform extensive simulations using a number of IEEE standard test cases of different scales (different number of buses). Our simulation results indicate that AMS enables the system to experience much lower amount of load shedding compared with an NE strategy.

Tags

Agent-based modeling intrusion detection Learning systems security Game