FastTrafficAnalyzer: An Efficient Method for Intrusion Detection Systems toAnalyze Network Traffic

Abstract

Network intrusion detection systems are software or devices used to detect malignant attackers in moderninternet networks. The success of these systems depends on the performance of the algorithm and methodused to catch attacks and the time it takes for it. Due to the continuous internet traffic, these systems areexpected to detect attacks in real time. In this study, using a proposed pre-processing, internet traffic databecomes more easily processable and traffic is classified by network analysis with machine learningtechniques. In this way, the traffic analysis time was significantly shortened and a high level of successwas achieved. The proposed model has been tested in the CSE-CIC-IDS2018 dataset and its advantagedverified. Experimental results i) 99.0% detection rate was achieved in the ExtraTree algorithm for binaryclassification, while a reduction of 82.96% was achieved in the processing time per sample; ii) Formulticlass (15 class) detection, 98.5% detection rate was achieved with the Random Forest algorithm,while a 64.43% shortening was achieved in the processing time per sample. As a result, similarclassification rate with the studies in the literature has been achieved with much shorter test time.