Adaptive Defending Strategy for Smart Grid Attacks

One active area of research in smart grid security focuses on applying game-theoretic frameworks to analyze interactions between a system and an attacker and formulate effective defense strategies. In previous work, a Nash equilibrium (NE) solution is chosen as the optimal defense strategy, which [7, 9] implies that the attacker has complete knowledge of the system and would also employ the corresponding NE strategy. In practice, however, the attacker may have limited knowledge and resources, and thus employ an attack which is less than optimal, allowing the defender to devise more efficient strategies. We propose a novel approach called an adaptive Markov Strategy (AMS) for defending a system against attackers with unknown, dynamic behaviors. The algorithm for computing an AMS is theoretically guaranteed to converge to a best response strategy against any stationary attacker, and also converge to a Nash equilibrium if the attacker is sufficiently intelligent to employ the AMS to launch the attack. To evaluate the effectiveness of an AMS in smart grid systems, we study a class of data integrity attacks that involve injecting false voltage information into a substation, with the goal of causing load shedding (and potentially a blackout). Our preliminary results show that the amount of load shedding costs can be significantly reduced by employing an AMS over a NE strategy.