Network Intrusion Detection System Using Machine Learning with Data Preprocessing and Feature Extraction

Abstract

Abstract: Unauthorized access to a computer network can be discovered by scanning the network traffic for evidence of malicious activity, which is what Network Intrusion Detection (NID) does. However, in this study, we will concentrate on the technology, development, and strategic importance that make up the large field of Network Intrusion Detection (NID). Many new strategies have been created in the last few years to help computer security specialists in protecting a single host or an entire network against unauthorized access, theft, and denial-of-service assaults, which are the primary causes of computer crime. Intrusion Detection is critical for both the military and commercial sectors since it is the most significant study area for the future networks' Information Security. In this paper, a model is being proposed, where the data is preprocessed before training with the algorithms. A study done by comparing with other models shows that, the current model built with Random Forest can outperform other existing models built with ANN when the data is preprocessed. After building model after data pre-processing and feature extraction, we are able to achieve 98.71% accuracy on NSL-KDD dataset.