Moving-Target-Defense-Based Dual-Mode Cybersecurity for loT-Enabled Active Suspension Systems Against Resourceful FDI Attacks
Mengni Du, Xiangpeng Xie, Chun Zhu, Heng Wang · IEEE Internet of Things Journal · 2025
In the Industry 4.0 era marked by cyber–physical systems (CPSs), the flourishing Internet of Things (IoT) technologies offer development potential for intelligent transportation systems. Physical devices in automobile systems are connected to the Internet to enable data collection and exchange. However, due to vulnerabilities in IoT devices and communication protocols, IoT-enabled CPSs are susceptible to attacks. As a core component of vehicle systems, malicious attacks on the vehicle’s active suspension can severely degrade passenger comfort and threaten vehicle safety. This article investigates the security of IoT-enabled active vehicle suspension systems (AVSSs) under resourceful attackers. First, a discrete-time Takagi–Sugeno fuzzy suspension model is developed to capture the inherent system uncertainties. Second, to counter resourceful false data injection (FDI) attackers who can acquire system knowledge and construct attacks, a dual-mode security algorithm integrating proactive and reactive defense mechanisms is proposed. The proactive defense mechanism leverages the moving target defense (MTD) strategy, which dynamically alters system parameters to hinder attackers’ reconnaissance capabilities. This increases attackers’ costs while reducing defenders’ burden in countering attacks. Furthermore, a high-order observer-based controller is designed to resist attacks, incorporating both the MTD strategy and a homogeneous polynomial parameter-dependent control law. On the other hand, the reactive defense mechanism employs an MTD-based detector to detect attacks. Finally, the proposed method is validated through simulations and hardware-in-the-loop (HIL) tests.