Enhancing Detection of Zero-Day Attacks in Cloud Infrastructure using Ensemble RBFNN with GAN-based Data Augmentation

Objectives: The main objective of this study was to develop a novel and intelligent framework by integrating the Generative Adversarial Network (GAN) based data augmentation technique with ensembled Radial Basis Functions Networks (RBFNNs). The goal was to address the challenges of scarcity of training data and false positive cases and efficiently detect zero-day intrusion attacks in real-time with higher precision and accuracy. Methods: This study has proposed a novel framework for developing an IDS model, which was designed by integrating an ensemble of Radial Basis Function Neural Networks (RBFNNs) with Generative Adversarial Network (GAN) based data augmentation. This model was trained and tested with popular benchmark real-world datasets, augmented with GAN-based synthetic data, and outperformed traditional classification models. Findings: The proposed model achieved an accuracy of 98.7% and under 1.2% false positive rate (FPR) for zero-day attack detection, and demonstrated superior performance, as compared to standalone classifiers like Random Forest, SVM, CNN, and standard RBFNN. Novelty: The results suggested that ensembled learning along with generative data augmentation can help in building adaptable, intelligent and resilient IDS systems. Keywords: Zero-day attack, Cloud Security, Resilient IDS, Ensemble Learning, GAN Data Augmentation