Novel GAN-based privacy-enhanced intrusion detection system for cyberattack classification

Abstract

This paper presents a Novel GAN-Based Privacy-Enhanced Intrusion Detection System (IDS) to address the challenges posed by IoT and IIoT technologies, which increase vulnerability to cyberattacks. Conventional IDS face issues like data imbalance, privacy concerns, and adapting to evolving attack patterns. The proposed model integrates Bidirectional Gated Recurrent Units (Bi-GRUs) with Generative Adversarial Networks (GANs). GANs generate synthetic data to mitigate data imbalance, enhancing the model's generalization capabilities, while Bi-GRUs ensure accurate classification of complex temporal attack patterns. The model achieves an impressive 99.96% accuracy, with high precision (99.34%), recall (99.54%), and F1-score (99.21%), outperforming existing methods like CNN+LSTM, RNN, and XG-Boost. The confusion matrix shows perfect classification for “DoS” and “Mirai” attacks, with minimal misclassifications between similar benign traffic types. The ROC curve's AUC of 0.99 and the closely aligned accuracy curves for training and validation further highlight the model's robustness. The study emphasizes the importance of GAN-based augmentation in balancing hostile and benign traffic classes and significantly improving classification accuracy for rare attack types. This approach addresses critical issues in data imbalance, privacy, and attack classification, marking significant progress in IDS development.