doi: 10.7763/JACN.2013.V1.1
Real-Time Computer Network Anomaly Detection Using Machine Learning Techniques
Kriangkrai Limthong
1,2
- 1 Department of Computer Engineering, School of Engineering, Bangkok University, Pathumtani 12120, Thailand.
- 2 Department of Informatics, Graduate University of Advanced Studies (Sokendai), Chiyoda-ku, Tokyo 101-8430, Japan
Abstract
Detecting a variety of anomalies in computer network, especially zero-day attacks, is one of the real challenges for both network operators and researchers. An efficient technique detecting anomalies in real time would enable network operators and administrators to expeditiously prevent serious consequences caused by such anomalies. We propose an alternative technique, which based on a combination of time series and feature spaces, for using machine learning algorithms to automatically detect anomalies in real time. Our experimental results show that the proposed technique can work well for a real network environment, and it is a feasible technique with flexible capabilities to be applied for real-time anomaly detection.
Keywords
- Multivariate normal distribution
- nearest neighbor
- one-class support vector machine
- unsupervised learning
How to Cite
Kriangkrai Limthong, "Real-Time Computer Network Anomaly Detection Using Machine Learning Techniques," Journal of Advances in Computer Networks, vol. 1, no. 1, pp. 1-5, 2013. https://doi.org/ 10.7763/JACN.2013.V1.1
Copyright & License
Copyright © 2013 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).