Machine learning based network intrusion detection system

Abstract

The rapid expansion and extensive usage of the internet have increased as more people connect to the network, amplifying the need for network security. Intrusion Detection Systems (IDS) are cybersecurity tools that oversee the status of software and hardware in a network environment. These systems are designed to analyze and safeguard the system while forecasting its behavior. IDS continue to grapple with enhancing detection accuracy, which is addressed by utilizing Machine Learning (ML) algorithms to identify intrusions. In this project, various machine learning algorithms are employed to predict different types of network attacks using a dataset that encompasses a range of network intrusions. The IDS not only forecasts malicious activities but also identifies the specific type of network under threat. Keywords: cc