Deep Learning-Driven DDoS Attack Detection and Security Control in Autonomous Ground Vehicle: A Restart Controller Approach

Abstract

This study investigates the security issues of networked control systems (NCSs) under distributed denial of service (DDoS) attacks. The DDoS attacks overwhelm a target system by flooding it with excessive fake traffic or requests, causing network congestion. This prevents the system from processing legitimate control requests and ultimately leads to system failure. First, to free up the substantial memory resources on the controller and mitigate the impact of distributed DDoS attacks on the performance of NCSs, we design a deep learning supervised DDoS attack detection and restart control mechanism that ensures that the duration of each attack is effectively limited to the tolerable interval of the system. In addition, a straightforward Lyapunov–Krasovskii functional (LKF) is formulated to analyze the stability of NCSs facing DDoS attacks under the restart control mechanism. Furthermore, it simplifies the stability analysis by avoiding complex calculations and diminishes the conservativeness of the stability criteria through the integration of sophisticated integral inequality techniques. Finally, simulations with an autonomous ground vehicle (AGV) model validate the practical benefits and effectiveness of the proposed theoretical advancements.