SegCoT: Dependable Intrusion Detection System based on Segment-wise CoTransformer for Ship Communication Networks

Modern vessels integrate a massive digital infrastructure and navigation-dependent operating systems, allowing for ship-to-shore and ship-to-ship collaborative communication. However, the heightened interconnection of various maritime infrastructures inevitably amplifies the risk of vessel navigation and communication. Existing intrusion detection techniques were usually built on individual network events, failing to account for the multi-event long-term dependency problem caused by the high latency and low bandwidth of ship communication networks, therefore cannot tackle sophisticated cyber-ship attacks, resulting in lower accuracy in intrusion detection. In this paper, we propose a dependable Intrusion Detection System(IDS) based on Segment-wise CoTransformer(SegCoT) to detect cyber-ship intrusion events, which primarily contains a two-stage Network Pattern Extraction Component (NPEC) and an Intrusion Event Identification Component (IEIC). The NPEC automates the extraction of long-term dependency of massive intrusion events employing a SegEvent-wise Attention (SEA). Furthermore, the extracted dependencies are leveraged by the IEIC for specific intrusion type detection from a spatio-temporal feature fusion perspective. Based on a cyber-ship dataset collected from real ocean-going vessels, the proposed model achieves 99% intrusion detection accuracy, outperforming the existing state-of-the-art approaches.