Wireless Fidelity (Wi-Fi) technology is widely used in the environment of Internet of Things (IoT), and the importance of security assessment has increased. Nowadays, Wi-Fi security assessment is based on security tools that are operated manually, and some methods face issues due to the lack of automation. In this study, we suggest a method for adapting Wi-Fi penetration testing in which a reinforcement learning (RL) agent interacts with the environment by choosing actions based on the current state to maximize the total reward received. We model a tabular Q-learning algorithm as an agent interacting with the wi-fi environment. The action space is made up of denial-of-service attacks, while the environment state vector includes parameters of the network and indicators of attack success, which all contribute to the reward function. The experiments show that the RL agent successfully finds vulnerabilities in the Wi-Fi Protected Access 2 (WPA2) and Wi-Fi Protected Access 3 (WPA3) protocols.
Tleuberdin et al. (Thu,) studied this question.