Detection of Data Injection Attacks on Decentralized Statistical Estimation
Or Ohev Shalom, Amir Leshem, Anna Scaglione, Angelia Nedić · 2018
This paper describes a distributed statistical estimation problem, corresponding to a network of agents. The network may be vulnerable to data injection attacks, in which the attackers' main goal is to steer the network's final state to a state of their choice. We show that the detection metric of the straightforward attack scheme proposed by Wu et. at in [1], is vulnerable to a more sophisticated attack. To overcome this attack we propose a novel metric that can be computed locally by each agent to detect the presence of an attacker in the network, as well as a metric that localizes the attackers in the network. We conclude the paper with simulations supporting our findings.