Data-Balancing Algorithm Based on Generative Adversarial Network for Robust Network Intrusion Detection

With the popularization and advancement of digital technology and network technology in recent years, cyber security has emerged as a critical concern. In order to defend against malicious attacks, intrusion detection systems (IDSs) increasingly employ machine learning models as a protection strategy. However, the effectiveness of such models is dependent on the algorithms and datasets used to train them. The present study uses five different supervised algorithms (Naïve Bayes, CNN, LSTM, BAT, and SVM) to implement the IDS machine learning model. A data-balancing algorithm based on a generative adversarial network (GAN) is proposed to mitigate the data imbalance problem in the IDS dataset. The proposed method, designated as GAN-BAL, is applied to the CICIDS 2017 dataset and is shown to improve both the recall rate and the accuracy of the trained IDS models.