Research on Attack Detection for Traffic Signal Systems Based on Game Theory and Generative Adversarial Networks - Kailong Li; Ke Pan; Weijie Xiu; Min Li; Zhonghe He; Li Wang (Applied Sciences)
With the rapid development of intelligent transportation systems and information technology, the security of road traffic signal systems has increasingly attracted the attention of managers and researchers. This paper proposes a new method for detecting attacks on traffic signal systems based on game theory and Generative Adversarial Networks (GAN). First, a game theory model was used to analyze the strategic game between the attacker and the defender, revealing the diversity and complexity of potential attacks. A Bayesian game model was employed to calculate and analyze the attacker’s choice of position. Then, leveraging the advantages of GAN, an adversarial training framework was designed. This framework can effectively generate attack samples and enhance the robustness of the detection model. Using empirical research, we simulated the mapping of real traffic data, road network data, and network attack data into a simulation environment to validate the effectiveness of this method. In a comparative experiment, we contrasted the method proposed in this paper with the traditional Support Vector Machine (SVM) algorithm, demonstrating that the model presented here can achieve efficient detection and recognition across various attack scenarios, with significantly better recall and F1 scores compared to traditional methods. Finally, this paper also discusses the application prospects of this method and its potential value in future intelligent transportation systems.