Exploring Various Intrusion Detection Methods Using Machine Learning Techniques

Abstract

In the modern world of security, it is very necessary to put in place intrusion detection systems (IDS) and intrusion prevention systems (IPS) that are both reliable and effective. The primary function of an intrusion detection system (IDS) is to identify unusual behavior in network traffic by making use of efficient techniques. In intrusion detection based on anomaly detection, the application of machine learning algorithms plays an essential part. The purpose of this study is to provide an overview of the machine learning approaches that are utilized in intrusion detection. For intrusion detection, classification techniques such as logistic regression, naive Bayes, KNN, Decision Tree, Random Forest, and Support Vector Machines (SVM) work well. In this research, we investigate the behavior and features of classification approaches that are based on machine learning and are utilized for applications related to intrusion detection.