Balancing the signals: Bayesian equilibrium selection for high-speed railway sensor defense

High-speed railway systems face frequent cybersecurity threats targeting their information networks. Continuous operation of protection mechanisms on wireless sensors results in persistent energy consumption, which is undesirable. To address this, we propose an optimal defense strategy selection approach for High-speed Railway Wireless Sensor Networks (HSR-WSNs) using an enhanced signaling game model. We first establish the basic elements and overall structure of the model, considering the presence of incomplete information and dynamic interactions between players. This paper provides background on signaling games, including theoretical concepts of equilibrium strategies and locations. We then develop a system to quantify payoffs for offensive and defensive tactics, incorporating distance and usage frequency weights. Finally, Long ShortTerm Memory (LSTM) and the public UNSW-NB15 dataset are leveraged to augment assumptions and reduce human subjectivity in results. Experiments demonstrate the model effectively identifies energy balance points for HSR-WSNs and determines appropriate protection strategies. The work contributes to enhancing the selection of effective defense techniques against network attacks in wireless sensor systems. This has significant practical implications in enabling optimized energy usage and functionality of secured sensor nodes.