Implementation and Evaluation of Intrusion Detection Systems using Machine Learning Classifiers on Network Traffic Data

Abstract

Strong Intrusion Detection Systems (IDS) are now essential given how much more crucial services and communication are being reliant on digital networks. Through the use of machine learning classifiers on network traffic data, this research shows the deployment and thorough evaluation of IDS. The first step of the study is to gather and preprocess a wide dataset of network traffic, which includes both legitimate and criminal operations. A high-dimensional feature set is produced when important information is extracted from the raw data using feature engineering techniques. In order to simulate the patterns of network traffic, a variety of machine learning methods are used, such as Decision Trees, Random Forests, Support Vector Machines, and Neural Networks. The models are also put to the test in a variety of situations, such as those with changing levels of network traffic, different kinds of attacks, and false-positive rates. Results show that machine learning-based IDS is more accurate than conventional rule-based systems at identifying and categorising network intrusions. Assessments are made of the models' ability to scale up and change to accommodate new threats. A thorough examination of IDS utilising machine learning classifiers on actual network traffic data is provided in this research, which, in turn, advances network security. The results highlight the value of machine learning in improving the precision and sturdiness of intrusion detection systems and protecting crucial network infrastructures from new cyber threats.