Research on Attack Detection for Traffic Signal Systems Based on Game Theory and Generative Adversarial Networks  
[PERSON]  
1 Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology,  
Beijing 100144, China; [EMAIL_ADDRESS] (W.X. ); [EMAIL_ADDRESS] (M.L. ); [PERSON]  
noting that substantial research opportunities still exist in the area of information security for urban traffic control systems. [PERSON] et al. (2019) proposed an intelligent traffic detection and recognition method based on an adaptive unknown input observer. [PERSON] et al. (2019) developed an anomaly detection model for traffic networks that employs non-parametric Long Short-Term Memory (LSTM) and parametric Gaussian processes for online data-driven analysis. [PERSON] et al. (2019) proposed a network intrusion detection method based on deep learning, which uses circumvention technology to enhance resistance to attacks. At the same time, it is clear that in order to reduce the damage caused by malicious attacks on the Internet-connected traffic flow detector equipment at intersections, the traffic flow of malicious nodes in the Internet of Things can be identified. [PERSON] et al. (2019) proposed a method for detecting malicious nodes in the Internet of Things based on autoencoders. Compared to traditional K-means and Gaussian mixture model approaches, the proposed method demonstrated improved model detection performance. [PERSON] et al. (2019) designed a novel adaptive attack compensator that allows a fuzzy controller equipped with a compensator to ensure the stability of the attacked system and the required control performance. The main problems and research results are summarized in Table 1.  
In summary, during the anomaly detection process, most researchers utilizing autoencoder-based methods through dimensionality reduction and clustering techniques are able to effectively distinguish between normal and abnormal traffic. However, there remain challenges in enhancing real-time capabilities and adapting to more complex attack scenarios. Issues such as how to respond to more hidden and complex attack methods, how to improve the system's robustness and adaptability, and how to better integrate IoT technology with security control strategies need to be addressed. Future research should focus on further improving and advancing these technologies to ensure the security and reliability of road traffic signal systems. This article focuses on the scientific research on attack detection in urban traffic signal control systems, with the main goals as follows:  
1. Since defenders cannot accurately understand the attack locations, this article transforms the game into a complete yet imperfect information game for analysis. It is necessary to calculate the probability of the TSM and UC being attacked, solve for the pure-strategy Bayesian-Nash equilibrium and mixed-strategy Bayesian-Nash equilibrium points of the attacker, clarify the true attack locations, and further select defense measures. 2. A generative adversarial network model is used to learn traffic flow data and current detection data to achieve anomaly detection and data recovery, generating three common types of network attack behaviors: denial of service, tampering attacks, and replay attacks. This approach aims to generate key target data attackers use to influence traffic control for their own purposes, while also achieving comparative effectiveness.