Network Intrusion Detection Systems Design: A Machine Learning Approach

With the increasing popularization of computer network-based technologies, security has become a daily concern, and intrusion detection systems (IDS) play an essential role in the supervision of computer networks. An employed approach to combat network intrusions is the development of intrusion detection systems via machine learning techniques. The intrusion detection performance of these systems depends highly on the quality of the IDS dataset used in their design and the decision making for the most suitable machine learning algorithm becomes a difficult task. The proposed paper focuses on evaluate and accurate the model of intrusion detection system of different machine learning algorithms on two resampling techniques using the new CICIDS2017 dataset where Decision Trees, MLPs, and Random Forests on Stratified 10-Fold gives high stability in results with Precision, Recall, and F1-Scores of 98% and 99% with low execution times.