Distributed joint spoofing attack identification and estimation in sensor networks

Distributed estimation of a deterministic scalar parameter by using quantized data in the presence of spoofing attacks, which modify the statistical model of the physical phenomenon, is considered. The paper develops an efficient heuristic approach to jointly detect attacks and estimate under spoofing attacks that are undetectable by a traditional approach that relies on noticing the data is not consistent with an expected family of distributions. Numerical results show that the proposed approach can correctly identify the attacked sensors with a large number of time observations, and moreover, the estimation performance of the proposed approach can asymptotically achieve the genie Cramer-Rao bound (CRB) for the desired parameter, which is the CRB under the assumption that the set of attacked sensors is known.