Game-theoretic physical layer authentication for spoofing detection in internet of things

被引:6
|
作者
Wu, Yue [1 ]
Jing, Tao [1 ]
Gao, Qinghe [1 ]
Wu, Yingzhen [1 ]
Huo, Yan [1 ]
机构
[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
基金
北京市自然科学基金; 中国国家自然科学基金;
关键词
Game theory; Physical layer authentication; Nash equilibrium; Spoofing detection; OPTIMIZATION; SECURITY; ATTACKS; GAIN;
D O I
10.1016/j.dcan.2022.12.016
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
The Internet of Things (IoT) has permeated various fields relevant to our lives. In these applications, countless IoT devices transmit vast amounts of data, which often carry important and private information. To prevent malicious users from spoofing these information, the first critical step is effective authentication. Physical Layer Authentication (PLA) employs unique characteristics inherent to wireless signals and physical devices and is promising in the IoT due to its flexibility, low complexity, and transparency to higher layer protocols. In this paper, the focus is on the interaction between multiple malicious spoofers and legitimate receivers in the PLA process. First, the interaction is formulated as a static spoof detection game by including the spoofers and receivers as players. The best authentication threshold of the receiver and the attack rate of the spoofers are consideblack as Nash Equilibrium (NE). Then, closed-form expressions are derived for all NEs in the static environment in three cases: multiplayer games, zero-sum games with collisions, and zero-sum games without collisions. Considering the dynamic environment, a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is proposed to analyze the interactions of receiver and spoofers. Last, comprehensive simulation experiments are conducted and demonstrate the impact of environmental parameters on the NEs, which provides guidance to design effective PLA schemes.
引用
收藏
页码:1394 / 1404
页数:11
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