Machine Learning-Driven Data Mining Techniques for Enhancing Cyber Security

Abstract

As cyberattacks become more complex and frequent, cybersecurity has emerged as a top research priority. Traditional signature-based defense systems struggle to identify new and evolving threats. To tackle this challenge, researchers are increasingly turning to machine learning and data mining techniques to uncover patterns, detect suspicious activities, and reveal hidden relationships within vast security datasets. This paper will take a deep dive into various data mining methods, including classification, clustering, association rule mining, and anomaly detection, and how they can be applied to enhance cybersecurity. Additionally, it will explore the integration of supervised, unsupervised, and ensemble learning approaches, such as Support Vector Machines, random forests, neural networks, and deep learning architectures, particularly in the contexts of intrusion detection, malware analysis, and fraud detection. The discussion will also cover benchmark datasets like KDD Cup 99, NSL-KDD, CICIDS 2017, and UNSW-NB15, along with performance metrics such as accuracy, precision, recall, F1-score, and AUC. Furthermore, the paper will address emerging challenges in the field, including class imbalance, concept drift, adversarial attacks, and scalability. Finally, it will outline promising research directions that focus on hybrid intelligent systems, explainable AI, and big data-driven cybersecurity analytics.

Keywords
Cybersecurity, Data Mining, Machine Learning, Intrusion Detection, Anomaly Detection, Supervised Learning, Unsupervised Learning, Cyber threat, Adversarial Attack.