SCGIN‐ID: A Self‐Supervised Contrastive Learning Framework For Graph‐Based Network Intrusion Detection

With the rapid development of Internet technologies, network security threats have significantly increased, emphasizing the need for accurate and efficient network intrusion detection systems (IDS). While traditional IDS methods rely on manual feature selection or deep learning, these approaches often fail to capture the full complexity of network data, especially the diverse structural and content‐related features. To address these limitations, this paper proposes a novel self‐supervised contrastive learning algorithm for network intrusion detection based on a graph isomorphism network ( SCGIN‐ID ). The method transforms network traffic into graph representations, converting the task into a node classification problem. By sampling positive and negative subgraphs centered on target nodes, the proposed approach leverages graph isomorphism networks to capture both structural and content‐based anomalies through self‐supervised contrastive learning. Extensive experiments on two benchmark datasets demonstrate that SCGIN‐ID achieves superior performance compared to five representative baseline models, providing an effective solution for comprehensive and accurate network intrusion detection.