DEEP LEARNING-BASED INTRUSION DETECTION SYSTEM FOR REAL-TIME CYBERSECURITY IN CLOUD COMPUTING ENVIRONMENTS

Abstract

The shift to cloud computing characterized by virtualization, multi-tenancy, elastic scaling, and on-demand resource provisioning has revolutionized IT infrastructure but simultaneously expanded the attack surface, enabling sophisticated threats such as VM escape, side-channel attacks, zero-day exploits, and advanced persistent threats that exploit dynamic resource allocation and co-location vulnerabilities. Traditional Intrusion Detection Systems (IDS), reliant on signature matching or shallow anomaly detection with manual feature engineering, struggle with the volume, velocity, and complexity of cloud-native traffic, often failing against novel or polymorphic attacks. This review explores the transformative role of deep learning (DL) in real-time cloud IDS, focusing on architectures including Convolutional Neural Networks (CNNs) for spatial pattern extraction, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) for temporal dependencies, Autoencoders for unsupervised anomaly detection, Generative Adversarial Networks (GANs) for synthetic attack generation and robust training, and Transformer-based models for attention-driven sequence analysis. DL approaches automate hierarchical feature learning from raw packet flows, logs, flow records, and system metrics, achieving detection accuracies of 96–99.5%, F1-scores >0.97, and significant reductions in false positives on benchmark datasets (CIC-IDS2017/2018, NSL-KDD, UNSW-NB15, CTU-13, and cloud-specific traces). Hybrid frameworks integrating DL with explainable AI (SHAP, LIME), federated learning for privacy-preserving multi-tenant detection, and edge-fog augmentation for low-latency inference are highlighted, alongside challenges: high computational overhead, adversarial robustness, concept drift in elastic environments, dataset imbalance, and interpretability demands in SOC operations. The convergence of DL with cloud-native security tools (e.g., container runtime introspection, serverless monitoring) positions intelligent, adaptive IDS as essential for proactive defense in hyper-scale, multi-tenant clouds.