Interrelated dynamic biased feature selection and classification model using enhanced gorilla troops optimizer for intrusion detection

Abstract

An Intrusion Detection System (IDS) is a valuable tool for network security since it can identify attacks, intrusions, and other types of illegal access. Excessive and irrelevant data slows down the classification process and eventually weakens the system's capacity to make informed decisions when IDS is monitoring a huge volume of network traffic. Innovative approaches are utilized to create large amounts of data and a lot of network traffic in order to test its effectiveness. A vital step in machine learning is feature selection. By determining which features are most essential for describing the dataset and its initial attributes, feature selection seeks to improve intrusion prediction performance while simultaneously delving deeper into the stored data. Making a feature selection is similar to fixing an optimization problem without a clear definition when users don't know where to begin. Finding the fewest characteristics required to describe the dataset, the original features, and to conduct classification is the primary purpose of feature selection, which also aims to improve prediction performance and acquire a deeper understanding of the stored data. As a result, researchers have been focusing on feature-selection issues recently, especially in light of the massive growth in available databases. Metaheuristic algorithms using a learning model have been the subject of studies to optimize feature selection difficulties. This research uses Enhanced Gorilla Troops Optimizer (EGTO), for enhancing the feature selection process and then performing classification. This research presents a Interrelated Dynamic Biased Feature Selection Model using Enhanced Gorilla Troops Optimizer (IDBFS-EGTO) for generation of feature vector set for intrusion detection. Despite its apparent success in handling a wide range of practical problems, it risks getting mired in local optima and premature convergence when faced with more difficult optimization challenges that is overcome with EGTO. The EGTO approach, which uses a collection of operators to strike a more steady equilibrium between exploitation and exploration. The proposed model generates relevant feature subset for machine learning model for accurate detection and classification of intrusions in the network. The proposed model achieved 98.4 % accuracy in intrusion detection and 98.6 % accuracy in EGTO optimization classification. The proposed model is improved by 3.8 % in feature weight allocation accuracy and 1.2 % in detection accuracy levels. The proposed model is compared with the traditional models and the results represent that the proposed model performance is high.