Traffic Characterization of Event-Triggered Multiagent Systems Under FDI Attacks

In this article, we investigate the triggering behaviors of periodic event-triggered multiagent systems (MASs) under multiplicative false data injection (FDI) attacks. An abstraction-based traffic model is established to characterize all possible triggering behaviors under arbitrary initial states, including the minimum interevent time (MIET) and the transition relations among IETs. We further answer the following two questions: 1) how FDI attacks affect the MIET and 2) how to select the sampling period for the anomalous MIET detection. As a potential application scenario, a behavior-based anomaly detection algorithm is developed based on the proposed traffic model to identify anomalous triggering behaviors caused by attacks. Simulations demonstrate the effectiveness and practical application of the proposed results.