Metaheuristics for Machine Learning

7Machine Learning-Based Intrusion Detection System with Tuned Spider Monkey Optimization for Wireless Sensor Networks

Ilavendhan Anandaraj¹* and Kaviarasan Ramu²

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

²Department of Computer Science and Engineering, RGM College of Engineering and Technology, Nandyal, India

Abstract

An intrusion detection system is used to look into the problem that occurs in a wireless sensor network (WSN) or system. Intrusion detection is software or a piece of hardware that looks for suspicious activity on a system or network. As computers become more connected to each other, intrusion detection becomes more important for network security. Different types of intrusion detection systems (IDSs) have been made to protect wireless sensor networks with the help of machine learning and statistical methods. Accuracy is the most important factor in how well an IDS works. The accuracy of intrusion detection needs to be improved to cut down on false alarms and raise the rate of detection. The main job of an IDS is to look at huge amounts of network traffic data. To solve this problem, a well-organized way to classify intrusion is needed. This issue is taken into account in this research. In this article, we suggest machine learning techniques such as the support vector machine (SVM) with the tuned spider monkey optimization (TSMO) for wireless sensor networks. The NSL-KDD knowledge discovery dataset is used to measure ...