Paper Title
Achieving the Highest Detection Rate and the Shortest Time in Building Model for Intrusion Detection in Computer Network

Intrusion detection systems (IDS) have developed into a very important research area among computer network security experts as a result of the expanding use of computer networks on the one hand and the rise in the number of attacks on computer networks on the other. In this study, a method utilizing machine learning algorithms is introduced that can detect the detection rate of secure traffic and the detection rate of Brute Force type attacks with 100% accuracy as well as the detection rate of Bot type attacks using the fewest possible identity features with more than 99% accuracy. The training and testing of the proposed methods utilized the CSE-CIC-IDS2018 data set, one of the most recent standard data sets for evaluating the effectiveness of computer network intrusion detection techniques. The CFSS ubset Eval feature selection method has allowed this method to employ less features while maintaining the same level of intrusion detection accuracy. It also takes less time to develop an intrusion detection model than other methods. Keywords - Computer Networks, Decision Tree, Feature Selection, Intrusion Detection System.