Application of Machine Learning Approaches in Intrusion Detection System

Authors

  • Zena Abdulmunim Aziz IT Department Technical College of Informatics Akre, Duhok Polytechnic University, Duhok, Kurdistan Region, IRAQ
  • Adnan Mohsin Abdulazeez Presidency of Duhok Polytechnic University, Duhok, Kurdistan Region, IRAQ

Keywords:

IDS, Attacks, Machine Learning, Support Vector Machine, Naive Bayes, J48

Abstract

The rapid development of technology reveals several safety concerns for making life more straightforward. The advance of the Internet over the years has increased the number of attacks on the Internet. The IDS is one supporting layer for data protection. Intrusion Detection Systems (IDS) offers a healthy market climate and prevents misgivings in the network. Recently, IDS is used to recognize and distinguish safety risks using Machine Learning (ML). This paper proposed a comparative analysis of the different ML algorithms used in IDS and aims to identify intrusions with SVM, J48, and Naive Bayes. Intrusion is also classified. Work with the KDD-CUP data set, and their performance has checked with the Weak software. In comparison of techniques such as J48, SVM and Naïve Bayes showed that the accuracy of j48 is the higher one which was (99.96%).

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Published

24-10-2021

Issue

Vol. 2 No. 2 (2021)

Section

Articles

How to Cite

Abdulmunim Aziz, Z. ., & Mohsin Abdulazeez, A. . (2021). Application of Machine Learning Approaches in Intrusion Detection System. Journal of Soft Computing and Data Mining, 2(2), 1-13. https://publisher.uthm.edu.my/ojs/index.php/jscdm/article/view/8634