Detection and Mitigation of Biasing Attacks on Distributed Estimation Networks
It addresses security vulnerabilities in cooperative state estimation for networked systems, but the contribution is incremental as it extends existing dissipativity-based methods.
The paper develops a method to detect and mitigate biasing attacks on distributed state estimation networks using a vector dissipativity framework, demonstrating its effectiveness through an example.
The paper considers a problem of detecting and mitigating biasing attacks on networks of state observers targeting cooperative state estimation algorithms. The problem is cast within the recently developed framework of distributed estimation utilizing the vector dissipativity approach. The paper shows that a network of distributed observers can be endowed with an additional attack detection layer capable of detecting biasing attacks and correcting their effect on estimates produced by the network. An example is provided to illustrate the performance of the proposed distributed attack detector.