A Novel IoT Intrusion Detection Model Using 2dCNN-BiLSTM - R. H. Xiang; S. S. Li; J. L. Pan (Radioengineering, 2024)
With the continuous advancement of Internet of Things (IoT) intelligence, IoT security issues have become more and more prominent in recent years. The research on IoT security has become a hot spot. A lightweight IoT intrusion detection model fusing a convolutional neural network, bidirectional long short-term memory network is proposed. It aims to improve processed data security and attack detection accuracy. First, sampling is performed by a hybrid sampling algorithm fusing SMOTE and ENN. Its aim is to minimize the impact of imbalanced-data and ensure data quantity in the process. Then, the data features are extracted by 2-dimensional convolutional neural network (2dCNN), and the effect of useless information is reduced by mean pooling and maximum pooling, so it can be adapted to the demanding resource environment of the IoT. On this basis, long-range dependent temporal features are extracted using bidirectional long short-term memory (BiLSTM), which aims to fully extract data features to improve detection accuracy in the limited resource environment. Finally, the algorithm is validated on the UNSW_NB15 dataset, and the results of the experiments reaches 93.5% at Accuracy, 86.4% at Precision, 85.3% at Recall and 85.8% at F1-Score. According to the results, the proposed algorithm can generate higher-quality samples, achieve higher detection rate with faster inference time and spend lower memory costs. This paper is part of special issue AI-DRIVEN SECURE COMMUNICATION IN MASSIVE IOT FOR 5G AND BEYOND.