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Since the advent of the Internet of Things (IoT), exchanging vast amounts ofinformation has increased the number of security threats in networks. As aresult, intrusion detection based on deep learning (DL) has been developed toachieve high throughput and high precision. Unlike general deep learning-basedscenarios, IoT networks contain benign traffic far more than abnormal traffic,with some rare attacks. However, most existing studies have been focused onsacrificing the detection rate of the majority class in order to improve thedetection rate of the minority class in class-imbalanced IoT networks. Althoughthis way can reduce the false negative rate of minority classes, it both wastesresources and reduces the credibility of the intrusion detection systems. Toaddress this issue, we propose a lightweight framework named S2CGAN-IDS. Theproposed framework leverages the distribution characteristics of networktraffic to expand the number of minority categories in both data space andfeature space, resulting in a substantial increase in the detection rate ofminority categories while simultaneously ensuring the detection precision ofmajority categories. To reduce the impact of sparsity on the experiments, theCICIDS2017 numeric dataset is utilized to demonstrate the effectiveness of theproposed method. The experimental results indicate that our proposed approachoutperforms the superior method in both Precision and Recall, particularly witha 10.2% improvement in the F1-score.

Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS