Network Anomaly Detection Using Unsupervised Machine Learning :Comparative study

  • Gheed Tawfeeq Waleed Department of Computer science , University of technology , Baghdad, Iraq .
  • Abeer Tariq Mawlood Department of Computer science , University of technology , Baghdad, Iraq .
  • Abdul Mohssen Jaber Department of Computer science , University of technology , Baghdad, Iraq .
Keywords: Network Intrusion Detection,, Unsupervised Machine Learning,, Clustering.

Abstract

The enormous growth in computer networks and in Internet usage in recent years, combined with the growth in the amount of data exchanged over networks, have shown an exponential increase in the amount of malicious and mysterious threats to computer networks. Machine Learning (ML) approaches have been implemented in the Network Intrusion Detection Systems (NIDS) to protect computer networks and to overcome network security issues. Anomaly detection has important applications in different domains such as fraud detection, intrusion detection, customer’s behavior and employee’s performance analysis. In this paper we have taken the Bank credit card dataset for finding Outlier detection. four Clustering methods have been compared and considered BIRCH Algorithm to be the best for finding noise and very effective for large datasets than the other clustering algorithms .

Downloads

Download data is not yet available.
Published
2019-09-25
How to Cite
Waleed, G., Mawlood, A., & Jaber, A. M. (2019). Network Anomaly Detection Using Unsupervised Machine Learning :Comparative study. Journal of Al-Qadisiyah for Computer Science and Mathematics, 11(4), Comp Page 1-. https://doi.org/10.29304/jqcm.2019.11.4.621
Section
Computer article