MCGAN: Modified Conditional Generative Adversarial Network (MCGAN) for Class Imbalance Problems in Network Intrusion Detection System

Abstract

With developing technologies, network security is critical, predominantly active, and distributed ad hoc in networks. An intrusion detection system (IDS) plays a vital role in cyber security in detecting malicious activities in network traffic. However, class imbalance has triggered a challenging issue where many instances of some classes are more than others. Therefore, traditional classifiers suffer in classifying malicious activities and result in low robustness to unidentified glitches. This paper introduces a novel technique based on a modified conditional generative adversarial network (MCGAN) to address the class imbalance problem. The proposed MCGAN handles the class imbalance issue by generating oversamples to balance the minority and majority classes. Then, the Bi-LSTM technique is incorporated to classify the multi-class intrusion efficiently. This formulated model is experimented on using the NSL-KDD+ dataset with the aid of accuracy, precision, recall, FPR, and F-score to validate the efficacy of the proposed system. The simulation results of the proposed method are associated with other existing models. It achieved an accuracy of 95.16%, precision of 94.21%, FPR of 2.1%, and F1-score of 96.7% for the NSL-KDD+ dataset with 20 selected features.

Keywords

• intrusion detection system
• deep convolution generative adversarial network
• class imbalance problem
• NSL-KDD dataset
• accuracy