Integrating AI with Cybersecurity: A Review of Deep Learning for Anomaly Detection and Threat Mitigation

Abstract

The rapidly evolving landscape of cyber threats poses significant challenges to traditional security measures, necessitating more advanced and adaptive approaches to anomaly detection and threat mitigation. This review paper explores innovative hybrid deep learning techniques that aim to address the limitations of existing cybersecurity solutions. Current approaches often struggle with the increasing sophistication of attacks, the expanding attack surface due to Internet of Things (IoT) and cloud adoption, and the overwhelming volume and velocity of network data. Moreover, traditional machine learning models frequently fall short in detecting novel threats, adapting to evolving attack patterns, and providing explainable results—critical factors in effective cybersecurity management. The review covers a spectrum of innovations, including: (1) ensemble methods that improve generalization and robustness against adversarial attacks; (2) hybrid deep learning models that excel in analyzing both spatial and temporal aspects of network behaviour; (3) autoencoder-based anomaly detection integrated with supervised classifiers for improved threat categorization; and (4) reinforcement learning-enhanced systems for dynamic, adaptive defence strategies. We also explore the application of explainable AI techniques to hybrid models, addressing the critical need for interpretability in security decisions.