An Accurate Approach for Intrusion Detection System Using Chaotic Maps, NPO, and SVM

The internet and technological advancements have facilitated faster communication and information sharing.However, cybercrime, including malware, phishing, and ransomware, remains a severe problem despite technical progress.Detecting the intrusion via Intrusion Detection System IDS in network communication and wireless networks WSN is a big challenge that grown with the rapid development of the technologies.The detection accuracy of the IDS mainly depends on the relevant features of the incoming data from the internet.Selecting the most relevant features within the optimal attributes is one of the primary stage of the machine learning and pattern recognition modules.Finding the feature subset from the present or existing features that will improve the algorithms' learning performance in terms of accuracy and learning time is the main goal of feature selection.Therefore, this paper proposes an accurate approach for intrusion detection in the network and WSN using machine learning methods include Chaotic Maps, Nomadic People Optimizer (NPO), and SVM.The proposed approach has five main stages which are: data collection, pre-processing, feature selection, classification, and evaluation.An improved version of NPO based on chaotic map called CNPO is proposed.The proposed CNPO uses chaotic maps to initialize the population and solution distribution.Besides, a proposed fitness function for CNPO based on SVM is proposed.The CNPO is employed for feature selection task by selecting only the most relevant features from the input dataset.The proposed approach evaluated using two datasets and achieve accuracy 99.96% and 99.98 for NSL-KDD, and WSN-DS respectively.