Enhancing cybersecurity in railways: Machine learning approaches for attack detection

Abstract

Ensuring the security of railway systems is crucial to protecting both passengers and infrastructure from the increasing threat of cyberattacks. As these threats grow in complexity and frequency, the need for resilient attack detection systems becomes more pressing.

This study tackles the challenge of attack detection in railway systems using machine learning techniques. By integrating Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), and Transfer Learning (TL), we enhance detection accuracy and develop a robust approach capable of identifying both known and emerging threats. Our methodology utilized the Electra Modbus dataset as the source domain and was transferred to the Electra S7Comm dataset as the target domain. Experimental results highlight the effectiveness of GAN-based data augmentation in mitigating the scarcity of attack samples, enhancing both the robustness and generalizability of our detection models. Additionally, the application of pre-trained models through transfer learning played a crucial role in achieving superior performance. Our proposed solution can be deployed within railway networks to strengthen security measures, enabling proactive responses to cyber threats, safeguarding critical infrastructure, and ensuring uninterrupted operations.