LAUSR

Networked radar systems are vulnerable to different types of attacks, including electronic countermeasure (ECM) jamming and false data injection (FDI) attack. Substantial research has concentrated on ECM jamming, which interferes with radar echoes between a radar and targets. However, FDI attack in which an attacker somehow replaces or modifies radars’ measurements, has rarely been considered. FDI attack is much stealthier than ECM jamming, making detection more difficult. In this paper, we take the first attempt to investigate the FDI attack's effects on a networked radar system. Further, we propose a novel data fusion algorithm to combat this attack. The proposed algorithm can dramatically reduce the attack's adverse effects, since it creatively introduces data's confidence factors into data fusion and adaptively decreases the fusion weights of the injected data. Numerical results verify the effectiveness of the proposed algorithm.