Role of Association Rule Mining Approach to Enhance Network Security for Cyber Threat Detection

Abstract

Now days cyber attacks are becoming more sophisticated in today's hyperconnected digital world, traditional security techniques are no longer enough. Using the UNSW-NB15 dataset, a well-known benchmark for network traffic analysis, this study provides a unique use of association rule mining for cyber threat identification. Our goal with association rule mining is to find patterns and connections between characteristics of network data that point to malicious conduct. Our research reveals an efficient way to detect attack signatures and unusual behavior using frequent itemsets and association rules. The findings demonstrate how this strategy may improve the precision with which cyberthreats are detected, providing a scalable and easily understood network security solution.