Distributed Consensus-Based Filtering Against False Data Injection Attacks

Existing distributed state estimation algorithms usually show satisfactory performance when dealing with data bias caused by network-induced phenomena. However, the security characteristics of these algorithms are often significantly affected by more complex and severe network attacks. Specifically, due to the lack of dynamic adaptability and abnormal data detection ability of the estimator, the estimator may deteriorate significantly or even diverge, which poses a serious threat to the stability and reliability of the system. To remedy this issue, we propose a distributed estimation algorithm based on the classical Kalman consensus filter framework. The accuracy of the estimator is significantly improved by utilizing the innovation of neighbor nodes. Furthermore, we construct an adaptive weight allocation mechanism based on the principle of minimizing the estimation error variance according to the possible accuracy differences between different estimators. This mechanism can evaluate the data accuracy of each node, and dynamically adjust its weight accordingly. Subsenquently, an event-triggered detector with random thresholds is designed to enhance the anti-attack ability of the estimator. The detector can monitor the data flow in the network in real time, and identify the potential abnormal or attack behavior by setting dynamic thresholds. Once abnormal data is detected, the detector can immediately trigger corresponding countermeasures to block the propagation path of erroneous data and protect the safe and stable operation of the system. Simulation results are employed to validate the effectiveness of the proposed method.