Performance Analysis of Network Traffic Intrusion Detection System Using Machine Learning Technique

Abstract

As the internet and communication areas evolve, from Wireless Sensor Networks (WSN) to the Internet of Things (IoT), network intrusions, and assaults become more common. For wireless sensor networks, this research study provides a progressive intrusion detection approach based on machine learning techniques. Wireless network traffic intrusions should be identified, studied, and removed from the network as quickly as possible. The purpose of an Intrusion Detection System (IDS) is to identify and prevent different intrusion attempts in the network and to provide users with a positive and secure connection. Machine learning and related approaches have recently evolved to identify network attacks. The major goal of the proposed study is to use network traffic to identify network intrusions in a smart and efficient manner. The proposed method for detecting network intrusion does not need any extra hardware. In order to protect the network against intrusion, the provided approach is used to guarantee the network's confidentiality and integrity. With other current approaches, the result achieves a greater detection rate.