ReMeNet: A Memory-Enhanced GAN Model for Intrusion Detection in Transportation Cyber-Physical Systems

Ensuring the safety and reliability of Transportation Cyber-Physical Systems (T-CPS) is critical. However, the increasing interconnectedness of T-CPS exposes them to sophisticated cyberattacks, necessitating robust intrusion detection systems (IDS) to safeguard against evolving threats. This paper aims to enhance the security of T-CPS by addressing two key challenges: effective anomaly detection and handling imbalanced datasets in intrusion detection tasks. In this paper, we propose ReMeNet (Reconstruction Memory Network), a novel intrusion detection model that combines a memory module with a GAN-based architecture to enhance anomaly detection and data reconstruction. To address the challenge of imbalanced datasets, we incorporate a Vector Quantized Wasserstein Generative Adversarial Network (VQ-WGAN) to generate additional samples for underrepresented attack categories, thereby balancing the dataset and improving detection performance. Experimental evaluation on the UNSW-NB15 dataset demonstrates that ReMeNet achieves an accuracy of 91.70%, and an F1-score of 91.63% which outperforms Random Forest by 8.02% and EC-GAN by 3.01%. The results show that ReMeNet effectively handles imbalanced data, improving detection rates across all attack categories in T-CPS.