A Systematic Review of Advanced Data Mining Techniques for Enhancing Cyber and Information Security

Abstract

The rapid escalation of cyber threats, driven by increasingly sophisticated attack vectors and the widespread digitization of organizational ecosystems, has intensified the demand for intelligent, adaptive, and data-driven security mechanisms. This systematic review examines the evolution, application, and effectiveness of advanced data mining techniques in enhancing cyber and information security across diverse technological environments. Drawing from a wide range of peer-reviewed studies, the review synthesizes findings on supervised, unsupervised, hybrid, and deep learning models used for intrusion detection, malware classification, behavioral analytics, phishing identification, and threat intelligence extraction. Emphasis is placed on the comparative performance of algorithms based on accuracy, precision, recall, scalability, false positive management, and real-time responsiveness. The review also highlights key challenges, including data imbalance, dataset limitations, adversarial machine learning threats, and computational constraints. Furthermore, emerging opportunities such as federated learning, adversarial resilience, cross-domain transfer learning, blockchain integration, IoT and edge-enabled security mechanisms, and predictive analytics–powered SOCs are explored. The findings indicate that while no single technique is universally superior, hybrid and ensemble models consistently offer more robust and adaptive defenses. Overall, this review contributes a comprehensive understanding of how data mining can strengthen cybersecurity strategies, guide future research pathways, and support the development of intelligent, proactive defense architectures.