Stealthy Attacks and Defense Countermeasures in Average Consensus Networks

This paper investigates the security issues of average consensus networks under false data injection attacks. Based on the intention of the adversary, the attacks to be analyzed are categorized into two types. The first type aims to rapidly diverge the states of the agents to infinity, while the second type seeks to mislead the states towards the adversary's desired values. A necessary and sufficient condition for Type-I attacks to bypass the anomaly detector is first provided, along with an algorithm for generating false data sequences. Subsequently, for Type-II attacks, the closed-form expression of the optimal attack is derived using dynamic programming. To address these vulnerabilities, a watermarking-based data transmission strategy is proposed by resorting to cryptographic pseudo-random sequences. The influence of watermarking parameters on detection performance across different attack scenarios is analyzed. It is demonstrated that the proposed strategy can effectively assist the anomaly detector in identifying stealthy attacks or mitigate its impact on the consensus networks by adjusting the watermarking parameters. A numerical example is provided to demonstrate the validity of the developed results.