A Lightweight Multi-Classification Intrusion Detection Model for Edge IoT Networks

Intrusion detection aims to effectively detect abnormal attacks in the Internet of Things (IoT) network, which is crucial for cybersecurity. However, traditional intrusion detection methods are difficult to effectively extract data features from traffic data, and most existing models are too complex to be deployed on edge servers. Addressing this need, this paper proposes a hybrid feature selection method and a lightweight deep learning intrusion detection model. Firstly, the data features space is reduced by using variance filtering, mutual information, and the Pearson Correlation Coefficient, thereby reducing the computational cost of subsequent model training. Then, an intrusion detection model based on Temporal Convolutional Network (TCN) is constructed. This model utilizes dilated causal convolutions to effectively capture long-term temporal dependencies in network traffic. Simultaneously, use the residual connections to mitigate the vanishing gradient problem, making the model easier to train and converge. Finally, experiments are conducted on the newly released Edge-IIoTset dataset. The results show that the proposed feature selection algorithm maintains good detection performance despite a significant reduction in feature dimensionality. Furthermore, compared with other models, the proposed TCN-based approach achieves higher classification accuracy with lower computational overhead, demonstrating its suitability for deployment in resource-constrained edge computing environments.