Advanced Data Mining Techniques for Strengthening Cyber and Information Security: A Comprehensive Analytical Study

Abstract

The rapid expansion of digital infrastructures, cloud environments, and interconnected systems has intensified the complexity and frequency of cyberattacks, making traditional signature-based security mechanisms increasingly inadequate. This study provides a comprehensive analytical examination of advanced data mining techniques and their role in strengthening cyber and information security. By systematically comparing supervised learning models, unsupervised clustering techniques, ensemble architectures, and deep neural networks, the research evaluates their performance across parameters such as accuracy, execution time, detection rate, false positives, scalability, and real-time applicability. Findings reveal that ensemble and hybrid models—particularly Gradient Boosting combined with neural networks—consistently deliver superior detection accuracy and reduced false alarms, making them highly suitable for modern intrusion detection systems. Deep learning approaches also demonstrate strong capability in identifying complex, non-linear attack patterns, while clustering techniques like SOM and DBSCAN prove effective for anomaly detection and zero-day threat identification. The study further highlights critical performance–complexity trade-offs, noting that advanced models require substantial computational resources despite their high predictive power. The research underscores that integrating multiple data mining techniques yields more robust and adaptable cyber defense solutions. These insights contribute to the development of intelligent, scalable, and proactive security architectures capable of responding effectively to evolving cyber threats.