Abstract
Software defined Internet of Vehicles (SD-IoV) is an emerging paradigm for accomplishing Industrial Internet of Things (IIoT). Unfortunately, SD-IoV still faces security challenges. Traditional solutions respond after attacks happening, which is low-effective. To cope with this problem, moving target defense (MTD) was proposed to modify network configurations dynamically. However, current MTD for IIoT has several drawbacks: 1) it cannot handle highly dynamic environments; 2) MTD strategy lacks intelligence because it needs attack–defense models; 3) they are difficult to trace sources. In this article, we propose an intelligent MTD scheme to defend against distributed denial-of-service in SD-IoV. Firstly, we model the configuration mutation of roadside units as a Markov decision process (MDP), and adopt deep reinforcement learning to solve the optimal configuration. Next, we evaluate the trust of vehicles after shuffling, which can distinguish spy vehicles. Finally, extensive simulation results confirm the effectiveness of our solution compared with representative methods.
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