We develop a new integrated communications and security (ICAS) design paradigm by leveraging the concept of reconfigurable intelligent surfaces (RISs). In particular, we propose RIS-assisted simultaneous transmission and secret key generation by sharing the RIS for these two tasks. Specifically, the legitimate transceivers intend to jointly optimize the data transmission rate and the key generation rate by configuring the phase-shift of the RIS in the presence of a smart attacker. We first derive the key generation rate of the RIS-assisted physical layer key generation (PLKG). Then, to obtain the optimal RIS configuration, we formulate the problem as a secure transmission (ST) game and prove the existence of the Nash equilibrium (NE), and then derive the NE point of the static game. For the dynamic ST game, we model the problem as a finite Markov decision process and propose a model-free reinforcement learning approach to obtain the NE point. Particularly, considering that the legitimate transceivers cannot obtain the channel state information (CSI) of the attacker in real-world conditions, we develop a deep recurrent Q-network (DRQN) based dynamic ST strategy to learn the optimal RIS configuration. The details of the algorithm are provided, and then, the system complexity is analyzed. Our simulation results show that the proposed DRQN based dynamic ST strategy has a better performance than the benchmarks even with a partial observation information, and achieves "one time pad" communication by allocating a suitable weight factor for data transmission and PLKG.

翻译：我们通过利用可重构智能表面（RIS）的概念，提出了一种新的集成通信与安全（ICAS）设计范式。具体而言，我们提出通过共享RIS资源实现RIS辅助的同步数据传输与密钥生成。在存在智能攻击者的情况下，合法收发双方旨在通过配置RIS的相移来联合优化数据传输速率与密钥生成速率。我们首先推导了RIS辅助物理层密钥生成（PLKG）的密钥生成速率。随后，为获得最优RIS配置，我们将该问题建模为安全传输（ST）博弈，证明了纳什均衡（NE）的存在性，并推导了静态博弈的NE点。针对动态ST博弈，我们将问题建模为有限马尔可夫决策过程，并提出一种无模型强化学习方法以获取NE点。特别考虑到实际环境中合法收发方无法获取攻击者的信道状态信息（CSI），我们开发了一种基于深度循环Q网络（DRQN）的动态ST策略来学习最优RIS配置。文中提供了算法细节，并进行了系统复杂度分析。仿真结果表明，即使在部分观测信息条件下，所提出的基于DRQN的动态ST策略仍优于基准方案，并通过为数据传输与PLKG分配合适的权重因子，实现了“一次一密”通信。