Res-TranBiLSTM: An intelligent approach for intrusion detection in the Internet of Things

Abstract

The Internet of Things (IoT), as the information carrier of the Internet and telecommunications networks, is a new network technology comprising physical entities embedded with electronic components, software and sensors, and characterized by strong complexity and openness. With the massive amount of data, the occurrence of network intrusion is also increasingly frequent, involving industrial control systems, IoT devices, mobile security, cloud services, and telecommunications services. With the diversification and intelligence of cyberattack behaviors, traditional intrusion detection systems (IDSs) face problems—such as insufficient feature extraction and inaccurate model classification—when faced with high-dimensional features and nonlinear massive data. Due to their powerful data representation learning ability, deep learning methods save substantial time in processing high-dimensional and complex intrusion data. On this basis, we propose an intrusion detection model using ResNet, Transformer and BiLSTM (Res-TranBiLSTM) that takes into account both the spatial and temporal features of network traffic. We use the Synthetic Minor Overriding Technique (SMOTE) – Edited Nearest Neighbor (ENN) method to alleviate the degree of data imbalance. In addition, we respectively establish a spatial feature extraction model based on ResNet and a temporal feature extraction model based on Transformer and BiLSTM to extract spatial features and temporal features parallelly. Finally, spatiotemporal features are included to achieve attack detection and classification. Further, simulation experiments are conducted using the public data sets NSL-KDD and CIC-IDS2017. The experiments are implemented using Python programming language and Pytorch framework. The results reveal that the performance of our proposed model is better than that of other models, with accuracy reaching 90.99%, 99.15% and 99.56%, on NSL-KDD dataset, CIC-IDS2017 dataset and MQTTset dataset, respectively. It increased the detection accuracy by about 1%-10% on NSL-KDD dataset and about 0.2%-10% on CIC-IDS2017 dataset, and about 1%-10% on MQTTset dataset. These results demonstrate that this method is effective in constructing and optimizing large-scale IDS in the IoT environment.