Replay attack detection for finite impulse response systems identification with binary‐valued observations: An input design approach

Abstract

AbstractThis paper investigates the replay attack detection problem for quantized finite impulse response (FIR) systems, taking a system identification perspective. Firstly, by establishing a replay attack model, the detectability of attacks for quantized FIR system identification is studied. After assessing the effect of replay attacks on system performance, appropriate attack detection metrics are selected. Algorithms for computing these detection metrics are designed, along with both offline and online detection methods. Subsequently, the detection algorithm's effectiveness is evaluated by using the probability of false detection and missed detection as performance metrics, with a discussion on the factors impacting its performance. Finally, the rationality of the proposed detection algorithm is validated through numerical simulations.