Intrusion Detection System using Metaheuristic Algorithm

Abstract

In most network management systems, the Intrusion Detection System (IDS) is utilized to detect and prevent suspicious activity. IDS fundamental concept is to use feature values from the capture function of the network packet to classify when an action is irregular. The bulk of conventional classification algorithms, however, are unable to classify unfamiliar behaviors. As a classification algorithm for IDS, the algorithm proposed in this paper would combine clustering, classification, and metaheuristic algorithms to create a high-performance classification algorithm named search economics with k-means support vector machine (SEKS) and search economics with the intrusion detection system (SEIDS). The IDS technique might improve the attack detection accuracy rate by combining methods for unsupervised learning and classification. SEKS and SEIDS are two phases that can be separated from the proposed system. In addition, the suggested algorithm's hybrid methodology aims to improve the accuracy of detecting abnormal activity in such a system, reduce the classification algorithm's processing time, and allow the IDS in a network context to detect unfamiliar and novel variant assaults. In terms of accuracy, the suggested approach beats all previous classification methods examined in this research.