Wireless communications are vulnerable to cyber attackers, which now have the flexibility to choose their type of attack. In this paper, combined with intelligent reflect surface (IRS), we jointly optimize base station beamforming and IRS reflected beamforming to counter smart attackers, thereby improving system security. Considering that attackers can flexibly choose their attack methods, such as jamming or eavesdropping, we make the base station intelligent by using reinforcement learning, which can predict the attack methods of attackers and choose whether to add artificial noise into the transmitted signals. At the same time, the interaction between the base station and the smart attackers are established as a non-cooperative game, the Nash equilibrium of the game is derived. Based on this, the base station anti-smart attackers strategy based on Deep Q-learning (DQN) is proposed, which can restrain the attack of the attacker to improve the security of the system. It can be verified from the simulation results that the proposed anti-smart attackers strategy can effectively enhance the secrecy rate of the wireless communication system, resist the attacker’s attack, and intelligently transmit artificial noise to improve system security.

中文翻译：

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基于强化学习的智能反射面辅助通信对抗智能攻击者

无线通信容易受到网络攻击者的攻击，他们现在可以灵活地选择攻击类型。在本文中，我们结合智能反射面（IRS），共同优化基站波束形成和IRS反射波束形成以对抗智能攻击者，从而提高系统安全性。考虑到攻击者可以灵活选择自己的攻击方式，如干扰或窃听，我们通过强化学习使基站智能化，可以预测攻击者的攻击方式，并选择是否在传输信号中加入人工噪声。同时，将基站与智能攻击者的交互建立为非合作博弈，推导出该博弈的纳什均衡。基于此，提出了基于深度Q学习（DQN）的基站反智能攻击策略，可以抑制攻击者的攻击，提高系统的安全性。从仿真结果可以验证，所提出的反智能攻击者策略可以有效提高无线通信系统的保密率，抵御攻击者的攻击，智能传输人工噪声，提高系统安全性。