The rapid evolution of cyber threats necessitates advanced techniques for timely and accurate detection. Data mining, a powerful tool for extracting patterns from large data sets, has shown great potential in enhancing cyber security measures. This study explores the application of data mining techniques in enhancing cybersecurity. The primary objectives were to identify effective data mining methods for threat detection, anomaly identification, and predictive risk analysis, while addressing challenges posed by evolving cyber threats.

Through an extensive literature review and recent case studies, this research explores techniques such as anomaly detection, classification, predictive modeling, and visual analytics. Data mining enables faster detection of cyber threats, reduces false positives, and improves incident response. Findings highlight the critical role of these techniques in mitigating advanced persistent threats (APTs), insider threats, and network intrusions.

The study concludes that integrating data mining tools, such as machine learning and clustering algorithms, into cybersecurity operations enhances proactive threat prevention and real-time monitoring. It recommends continuous adoption of emerging technologies, workforce training, and privacy-preserving frameworks to address current cybersecurity challenges.