부산대학교 도서관

An Intrusion Detection System (IDS) is a tracking system that recognizes suspicious activity and generates alerts when it discovers it.A current intrusion detection system’s major purpose is to identify signature and anomaly-based invasive attacks that abuse database privileges. In this work, we proposed a hybrid meta-heuristic clustering technique employing the Dragonfly and Cat swarm algorithms for building user profiles based on prior behavior. Furthermore, We explored the use of the CloFAST algorithm to mine association rules, which are then used to calculate the proposed dynamic conformance index, thus detecting fraudulent database transactions in a stream of incoming transactions. Experimental results demonstrate that the proposed technique achieves an accuracy of 99.33 % using a synthetically produced dataset in line with the TPC-C benchmark.