An Enhanced Intrusion Detection System for IoT Networks Based on Deep Learning and Knowledge Graph

Nowadays, the intrusion detection system (IDS) plays a crucial role in the Internet of Things (IoT) networks, which could effectively protect sensitive data from various attacks. However, the existing works have not considered multiview features fusion and failed to capture the semantic relationships among the anomalous requests. They are not robust and cannot detect the attack types in real-time. This paper proposes a lightweight intrusion detection system based on deep learning and knowledge graph. First, our system extracts semantic relationships and key features by knowledge graph and statistical analysis. Then, IoT network requests are converted into word vectors through multiview feature fusion and feature alignment. Finally, an attention-based CNN-BiLSTM model is designed to identify malicious request attacks, which can capture long-distance dependence and contextual semantic information. Experiment results show that the proposed model significantly outperforms the existing solution in the robustness of the model. Moreover, it can select more critical features for IDS to achieve better accuracy and lower the false alarm rate. Compared with the state-of-the-art systems, the proposed IDS achieves a higher detection accuracy of 90.01%. In addition, our system can detect various stealthy attack types (including DoS, Probe, R2L, and U2L) and extract semantic relationships among features.