Integrated Approach for Network Intrusion Detection

Abstract

This chapter explores into an integrated approach aimed at enhancing Network Intrusion Detection Systems (NIDS) by amalgamating the K-nearest neighbors (KNN) algorithm, Karhunen-Loève Transform (KLT), and Genetic Algorithm (GA) optimization. This collaboration is designed to tackle the challenges associated with accurately detecting various types of network intrusions while minimizing false positives. The KNN algorithm provides efficient classification, while the KLT reduces the dimensionality of the feature space, thereby enhancing computational efficiency. Additionally, GA optimization fine-tunes the parameters of the NIDS, thereby improving detection accuracy and adaptability to dynamic network environments. Consequently, this approach contributes to enhancing the network security of a system.