DM-IDS—A Network Intrusion Detection Method Based on Dual-Modal Fusion

The machine learning-based approach to network intrusion detection presents a groundbreaking research paradigm, positioned to replace traditional rule-based and signature-based methods. However, prior research methodologies have predominantly focused on flow-based approaches, which may not be effective in detecting all types of attacks at a granular level. In this study, we introduce DM-IDS, an attention-convolution architecture model for bimodal network intrusion detection in both flow and payload modalities, using bilinear fusion. Notably, we present a novel method for constructing binary-form feature vectors under the payload modality, with the goal of extracting additional security semantic features. To facilitate this, we independently develop a feature generation tool named Beeman. Finally, we conduct a series of comparative and ablation experiments on two publicly available datasets, CICIDS-2017 and CICIoT-2023, achieving state-of-the-art model performance.