doi: 10.7763/IJCTE.2009.V1.87
A Hybrid Intelligent Approach for Automated Alert Clustering and Filtering in Intrusion Alert Analysis
- Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia.
- Manuscript receivedJune 1, 2009
Abstract
As security threats change and advance in a drastic way, most of the organizations implement multiple Network Intrusion Detection Systems (NIDSs) to optimize detection and to provide comprehensive view of intrusion activities. But NIDSs trigger a massive amount of alerts even for a day and overwhelmed security experts. Thus, automated and intelligent clustering is important to reveal their structural correlation by grouping alerts with common attributes. We propose a new hybrid clustering model based on Improved Unit Range (IUR), Principal Component Analysis (PCA) and unsupervised learning algorithm (Expectation Maximization) to aggregate similar alerts and to reduce the number of alerts. We tested against other unsupervised learning algorithms to validate the performance of the proposed model. Our empirical results show using DARPA 2000 dataset the proposed model gives better results in terms of the clustering accuracy and processing time.
Keywords
- alert clustering and filtering
- Expectation Maximization
- Principal Component Analysis
- unsupervised learning
How to Cite
Maheyzah Md Siraj, Mohd Aizaini Maarof, and Siti Zaiton Mohd Hashim, "A Hybrid Intelligent Approach for Automated Alert Clustering and Filtering in Intrusion Alert Analysis," International Journal of Computer Theory and Engineering, vol. 1, no. 5, pp. 539-545, 2009. https://doi.org/10.7763/IJCTE.2009.V1.87
Copyright & License
Copyright © 2009 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).