Resilient Distributed $H_\infty$ Estimation via Dynamic Rejection of Biasing Attacks
For control systems requiring security against cyber-attacks, this work provides a resilient estimation framework, though it is incremental as it extends existing H∞ estimation to a specific attack model.
The paper tackles distributed H∞ estimation under biasing attacks, proposing a novel algorithm with attack detection filters that enables unbiased estimation despite compromised nodes. The method is decentralized and real-time computable.
We consider the distributed $H_\infty$ estimation problem with additional requirement of resilience to biasing attacks. An attack scenario is considered where an adversary misappropriates some of the observer nodes and injects biasing signals into observer dynamics. Using a dynamic modelling of biasing attack inputs, a novel distributed state estimation algorithm is proposed which involves feedback from a network of attack detection filters. We show that each observer in the network can be computed in real time and in a decentralized fashion. When these controlled observers are interconnected to form a network, they are shown to cooperatively produce an unbiased estimate the plant, despite some of the nodes are compromised.