A deep generative adversarial framework with rat-optimized feature selection for efficient intrusion detection in large-scale cloud network traffic environments

Abstract

Abstract Cloud computing is a fundamental paradigm in modern computing; however, its distributed and multi-tenant nature increases exposure to cyber threats, making effective intrusion detection essential. Despite recent advances in artificial intelligence–based intrusion detection systems (IDS), many existing approaches remain sensitive to class imbalance, computationally demanding, and insufficiently robust under large-scale network traffic conditions. To address these challenges, this paper proposes an AI-driven intrusion detection framework, termed Secured Deep Generative Adversarial Rat-Optimized Diagonal Matrix (SDGA-RODM), designed for efficient intrusion detection in large-scale cloud network traffic environments. The proposed framework integrates three key components: (i) a mean divergence regularization (MDR)–based preprocessing strategy to mitigate class imbalance without altering intrinsic data distributions; (ii) a rat-optimized diagonal matrix score (RODMS)–based feature selection mechanism to derive compact and discriminative feature subsets; and (iii) a Wasserstein generative adversarial learning–based classifier (DWGALC) to enhance robustness under complex and imbalanced attack patterns. Extensive experiments conducted on the LUFlow and CIC-IDS-2017 benchmark datasets demonstrate that SDGA-RODM achieves improved performance compared with the evaluated representative IDS methods, including deep learning, ensemble-based, and optimization-driven approaches, achieving up to 6–8% improvement in detection accuracy, 15–40% reduction in classification error, and up to 30% reduction in training time under imbalanced traffic conditions. These results indicate that SDGA-RODM provides an effective and computationally efficient intrusion detection framework, demonstrating promising performance and robustness under controlled benchmark and simulation-based settings representative of cloud network traffic environments.