Maximum Distortion Attacks in Electricity Grids

Iñaki Esnaola (1) , Samir M. Perlaza (2) , H. Vincent Poor (3) , Oliver Kosut (4)

1 University of Sheffield [Sheffield]
2 SOCRATE - Software and Cognitive radio for telecommunications
3 Princeton University
4 ASU - Arizona State University [Tempe]

Multiple attacker data injection attack construction in electricity grids with minimum-mean-square-error state estimation is studied for centralized and decentralized scenarios. A performance analysis of the trade-off between the maximum distortion that an attack can introduce and the probability of the attack being detected by the network operator is considered. In this setting, optimal centralized attack construction strategies are studied. The decentralized case is examined in a game-theoretic setting. A novel utility function is proposed to model this trade-off and it is shown that the resulting game is a potential game. The existence and cardinality of the corresponding set of Nash Equilibria (NEs) of the game is analyzed. Interestingly, the attackers can exploit the correlation among the state variables to facilitate the attack construction. It is shown that attackers can agree on a data injection vector construction that achieves the best trade-off between distortion and detection probability by sharing only a limited number of bits offlline. For the particular case of two attackers, numerical results based on IEEE test systems are presented.

Keywords: MMSE estimation; Data-injection attacks; de-centralized attacks; game theory

Domains: Information Theory [math.IT]; Signal and Image Processing

Iñaki Esnaola, Samir M. Perlaza, H. Vincent Poor, Oliver Kosut. Maximum Distortion Attacks in Electricity Grids. IEEE Transactions on Smart Grid, 2016, 7 (4), pp.2007-2015. ⟨10.1109/TSG.2016.2550420⟩. ⟨hal-01343248⟩