HRNN: Hypergraph Recurrent Neural Network for Network Intrusion Detection

In intrusion detection systems, deep learning has demonstrated its capability to effectively mine flow representations, significantly enhancing the ability to detect anomalies. However, current approaches still suffer from limitations in flow feature extraction and may require fine-tuning on different forms of data, and may even be nontransferable. The task of accurately and efficiently handling multiple forms of flow remains a challenging endeavor. In this work, we propose the Hypergraph Recurrent Neural Network (HRNN), a novel intrusion detection method that leverages the hypergraph higher-order structure and recurrent network. We construct flow data as hypergraph structures, which allow for more abundant information representation and implicitly incorporate more similar information in the model. The recurrent module extracts temporal features of the flow. Our design effectively fuses representations imbued with rich spatial and temporal semantics. Evaluations of several publicly available datasets portray that HRNN outperforms other state-of-the-art methods.