Abstract
Frequent communications among massive terminal devices are ubiquitous in forthcoming 5G Internet of Thing (IoT) networks. It strengthens links of massive machine-type-communication (MMTC), pushes forward the process of Internet of everything. However, due to continual interactions among different devices and the broadcast characteristic of wireless channels, it also brings new security challenges. Recently, physical layer security launches a new solution to guarantee information theoretic security. To enhance the physical layer security performance of massive intelligent devices, especially in D2D communications, the game theory and machine learning methods are introduced. In this paper, we first review physical layer security problems on D2D communications under different attack scenarios. Game theory is proposed to describe hierarchical and heterogeneous interactions among legitimate users and active attackers in 5G IoT networks, then some distributed machine learning methods are proposed to obtain equilibrium states among different agents. Moreover, numerical results are provided to verify availability and efficiency of proposed game-theoretic learning approaches. Finally, we discuss open issues and future research directions in term of anti-eavesdropping and anti-jamming problems in D2D communications when facing active attackers.
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Luo, Y., Yang, Y., An, S., Feng, Z. (2021). Secure and Reliable D2D Communications with Active Attackers: A Game-Theoretic Perspective and Machine Learning Approaches. In: Guan, M., Na, Z. (eds) Machine Learning and Intelligent Communications. MLICOM 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-030-66785-6_55
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