| Title | Game-theoretic and Learning-aided Physical Layer Security for Multiple Intelligent Eavesdroppers |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Wu, Yingzhen, Huo, Yan, Gao, Qinghe, Wu, Yue, Li, Xuehan |
| Conference Name | 2022 IEEE Globecom Workshops (GC Wkshps) |
| Date Published | dec |
| Keywords | artificial intelligence, Collaboration, composability, compositionality, Deep Learning, game theory, Games, Human Behavior, human factors, information theoretic security, Metrics, Multiple intelligent eavesdroppers, Nash equilibrium, physical layer security, policy-based governance, Protocols, pubcrawl, resilience, Resiliency, Scalability, Wireless communication |
| Abstract | Artificial Intelligence (AI) technology is developing rapidly, permeating every aspect of human life. Although the integration between AI and communication contributes to the flourishing development of wireless communication, it induces severer security problems. As a supplement to the upper-layer cryptography protocol, physical layer security has become an intriguing technology to ensure the security of wireless communication systems. However, most of the current physical layer security research does not consider the intelligence and mobility of collusive eavesdroppers. In this paper, we consider a MIMO system model with a friendly intelligent jammer against multiple collusive intelligent eavesdroppers, and zero-sum game is exploited to formulate the confrontation of them. The Nash equilibrium is derived by convex optimization and alternative optimization in the free-space scenario of a single user system. We propose a zero-sum game deep learning algorithm (ZGDL) for general situations to solve non-convex game problems. In terms of the effectiveness, simulations are conducted to confirm that the proposed algorithm can obtain the Nash equilibrium. |
| DOI | 10.1109/GCWkshps56602.2022.10008668 |
| Citation Key | wu_game-theoretic_2022 |
Game-theoretic and Learning-aided Physical Layer Security for Multiple Intelligent Eavesdroppers