Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection

Abstract

A new supervised machine learning system is made to figure out whether network traffic is harmful or not. A combination of the supervised learning algorithm and the feature selection method has been used to find the best model based on how well it can detect. This study shows that Artificial Neural Network (ANN)-based machine learning with wrapper feature selection does a better job of classifying network traffic than the support vector machine (SVM) method. supervised machine learning techniques like SVM and ANN are used to classify network traffic from the NSL-KDD dataset in order to measure performance. Comparative studies show that the proposed model is better at detecting intrusions than other models that are already out there.