Learning-Based Cooperative False Data Injection Attack and Its Mitigation Techniques in Consensus-Based Distributed Estimation

For the false data injection attack (FDIA) in the consensus-based distributed estimation, the information of weight matrix is critical for both attackers and defenders. In this paper, we study the impact of the weight matrix on the FDIA and the attack mitigation techniques. We first propose a learning-based cooperative FDIA strategy, where malicious nodes acquire the information of weight matrix cooperatively and launch the attack covertly. Two types of FDIA, the sudden FDIA and the dynamic FDIA, are considered to tamper the consensus result of the network to a pre-designed false value. Moreover, using the obtained information of weight matrix, a real-time surveillance and response mechanism is constructed to enhance the covertness of FDIA strategy. Since the surveillance and response mechanism can bypass existing FDIA detection methods, we further investigate the attack mitigation techniques against the learning-based cooperative FDIA. A real-time FDIA detection method and a reassessment mechanism with a punishment scheme are presented to resist the surveillance and response mechanism in the learning-based cooperative FDIA. Comprehensive simulation results verify that the attacker can obtain the information of weight matrix, and tamper the consensus result to a false value by launching the learning-based cooperative FDIA. And the real-time FDIA detection method can detect the malicious nodes efficiently and promptly.