Empirical Analysis of Machine Learning Techniques for Intrusion Detection in Network System

Abstract

Abstract—Increasing network resource usage creates security risks with it. Malwares and other sources may disrupt the system operations and inadequate security holes in systems. Intrusion Detection System(IDS) is invented to alert admins in case of such security breaches. In order to enhance IDS systems, artificial intelligence as well as . In this research, literature studies employing CSE-CIC IDS- 2018, UNSW-NB15, ISCX-2012, NSLKDD and CIDDS-001 data sets, frequently used to design IDS systems, updated in detail. In addition, max-min normalisation was done on these data sets and classed created utilising ,K NN algo, vector support machine (SVM), Decision Tree (DT) algorithms, which among the most ancient ML approaches. As a result, some genuinely good results have been analyzed. Index Terms—Intrusion, Machine Learning, Security