# A Hybrid Security Framework for Train-to-Ground (T2G) Communication Using DOA-Optimized BPNN Detection, Bayesian Risk Scoring, and RL-Based Response

## TL;DR

This paper introduces a hybrid security framework for train-to-ground communication systems using optimized neural networks, Bayesian risk scoring, and reinforcement learning to detect and respond to threats.

## Contribution

The novel framework combines DOA-optimized BPNN detection, Bayesian risk scoring, and RL-based response for enhanced T2G communication security.

## Key findings

- The DOA-BPNN model improves anomaly detection performance in T2G communication.
- Bayesian risk scoring reduces system risk scores by 63.56%.
- Reinforcement learning enables dynamic selection of optimal defense actions.

## Abstract

With the widespread adoption of wireless communication technologies in modern high-speed rail systems, the Train-to-Ground (T2G) communication system for Electric/Diesel Multiple Units (EMU/DMU) has become essential for train operation monitoring and fault diagnosis. However, this system is increasingly vulnerable to various cyber-physical threats, necessitating more intelligent and adaptive security protection mechanisms. This paper presents an intelligent security defense framework that integrates intrusion detection, risk scoring, and response mechanisms to enhance the security and responsiveness of the T2G communication system. First, feature selection is performed on the TON_IoT dataset to develop a Dream Optimization Algorithm (DOA)-optimized backpropagation neural network (DOA-BPNN) model for efficient anomaly detection. A Bayesian risk scoring module then quantifies detection outcomes and classifies risk levels, improving threat detection accuracy. Finally, a Q-learning-based reinforcement learning (RL) module dynamically selects optimal defense actions based on identified risk levels and attack patterns to mitigate system threats. Experimental results demonstrate improved performance in both multi-class and binary classification tasks compared to conventional methods. The implementation of the Bayesian risk scoring and decision-making modules leads to a 63.56% reduction in system risk scores, confirming the effectiveness and robustness of the proposed approach in an experimental environment.