Biblio

In this paper, the distributed security control problem of connected vehicle systems (CVSs) is investigated under denial of service (DoS) attacks and uncertain dynamics. DoS attacks usually block communication channels, resulting in the vehicle inability to receive data from the neighbors. In severe cases, it will affect the control performance of CVSs and even cause vehicle collision and life threats. In order to keep the vehicle platoon stable when the DoS attacks happen, we introduce a random characteristic to describe the impact of the packet loss behavior caused by them. Dependent on the length of the lost packets, we propose a security platoon control protocol to deal with it. Furthermore, the security platoon control problem of CVSs is transformed into a stable problem of Markov jump systems (MJSs) with uncertain parameters. Next, the Lyapunov function method and linear matrix inequations (LMI) are used to analyze the internal stability and design controller. Finally, several simulation results are presented to illustrate the effectiveness of the proposed method.

The rapid developments of mobile communication and wearable devices greatly improve our daily life, while the massive entities and emerging services also make Cyber-Physical System (CPS) much more complicated. The maintenance of CPS security tends to be more and more difficult. As a ”gamechanging” new active defense concept, Moving Target Defense (MTD) handle this tricky problem by periodically upsetting and recombining connections between users and servers in the protected system, which is so-called ”shuffle”. By this means, adversaries can hardly obtain enough time to compromise the potential victims, which is the indispensable condition to collect necessary information or conduct further malicious attacks. But every coin has two sides, MTD also introduce unbearable high energy consumption and resource occupation in the meantime, which hinders the large-scale application of MTD for quite a long time. In this paper, we propose a novel deep reinforcement learning-based MOTAG system called DQ-MOTAG. To our knowledge, this is the first work to provide self-adaptive shuffle period adjustment ability for MTD with reinforcement learning-based intelligent control mechanism. We also design an algorithm to generate optimal duration of next period to guide subsequent shuffle. Finally, we conduct a series of experiments to prove the availability and performance of DQ-MOTAG compared to exist methods. The result highlights our solution in terms of defense performance, error block rate and network source consumption.