An Advanced Filter-based Supervised Threat Detection Framework on Large Databases

Abstract

Adaptive and robust detection mechanisms are becoming more and more necessary as cyber threats become more complex. This study presents a framework to increase threat detection efficiency and address the complex problems posed by various dynamic cyber threats. This study focuses primarily on investigating a new algorithm for feature classification and selection in predictive modeling applications. Using a sizable real-time threat detection dataset, a hybrid filter-based feature ranking and cluster-based classification approach is proposed. A detailed analysis was carried out to investigate the performance of the proposed algorithm and compare it with various machine-learning models. This study also examines how well the algorithm scales to large-scale datasets and adapts to different data properties. The results highlight the algorithm's potential to enhance the efficiency of predictive modeling by optimizing feature selection procedures and reducing model complexity, thus making a substantial contribution to the field of data-driven decision-making and the wider range of machine-learning applications.