Detecting Temporal Attacks: An Intrusion Detection System for Train Communication Ethernet Based on Dynamic Temporal Convolutional Network

The train communication Ethernet (TCE) of modern intelligent trains is under an ever-increasing threat of serious network attacks. Denial of service (DoS) and man in the middle (MITM), the two most destructive attacks against TCE, are difficult to detect by conventional methods. Aiming at their highly time-correlated properties, a novel dynamic temporal convolutional network-based intrusion detection system (DyTCN-IDS) is proposed in this paper to detect these temporal attacks. A semiphysical TCE testbed that is capable of simulating real situations in TCE-based trains is first built to generate an effective dataset for training and testing. DyTCN-IDS consists of two phases, and in the first phase, systematic feature engineering is designed to optimize the dataset. In the second phase, a basic detection model that is good at dealing with temporal features is first built by utilizing the temporal convolutional network with several architectural optimizations. Then, in order to decrease the computational consumption waste on network packet sequences with different lengths of inner temporal relationships, dynamic neural network technology is further adopted to optimize the basic detection model. Diverse experiments were carried out to evaluate the proposed system from different angles. The experimental results indicate that our system is easy to train, converges fast, costs less computational resources, and achieves satisfying detection performance with a macro false alarm rate of 0.09%, a macro F-score of 99.39%, and an accuracy of 99.40%. Compared to some canonical DL-based IDS and some latest IDS, our system acquires the best overall detection performance as well.

Automated summary

A novel dynamic temporal convolutional network-based intrusion detection system (DyTCN-IDS) is proposed to detect serious network attacks on modern intelligent trains. The system achieved satisfying detection performance with optimized dataset and computational efficiency.