Abstract

In this paper, we introduce a new clustering algorithm, FCC, for intrusion detection based on the concept of fuzzy connectedness. This concept was introduced by Rosenfeld in 1979 and used with success in image segmentation; here we extend this approach to clustering and demonstrate its effectiveness in intrusion detection. Starting with a single or a few seed points in each cluster, all the data points are dynamically assigned to the cluster that has the highest fuzzy connectedness value (strongest connection). With an efficient heuristic algorithm, the time complexity of the clustering process is O(NlogN), where N is the number of data points. The value of fuzzy connectedness is calculated using both the Euclidean distance and the statistical properties of clusters. This unsupervised learning method allows the discovery of clusters of any shape. Application of the method in intrusion detection demonstrates that it can detect not only known intrusion types, but also their variants. Experimental results on the KDD-99 intrusion detection data set show the efficiency and accuracy of this method. A detection rate above 94% and a false alarm rate below 4% are achieved, outperforming major competitors by at least 5%.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.