An Efficient Clustering Scheme to Exploit Hierarchical Data in Network Traffic Analysis

Authors:
Abdun Naser Mahmood;Christopher Leckie;Parampalli Udaya
Affiliations:
-;-;-
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
2008

Citing 0
Cited 5

Critical infrastructure protection: Resource efficient sampling to improve detection of less frequent patterns in network traffic

Journal of Network and Computer Applications
Synthetic security policy generation via network traffic clustering

Proceedings of the 3rd ACM workshop on Artificial intelligence and security
A clustering based system for instant detection of cardiac abnormalities from compressed ECG

Expert Systems with Applications: An International Journal
Inter cluster distance management model with optimal centroid estimation for K-means clustering algorithm

WSEAS TRANSACTIONS on COMMUNICATIONS
Compressed ECG Biometric: A Fast, Secured and Efficient Method for Identification of CVD Patient

Journal of Medical Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

There is significant interest in the data mining and network management communities about the need to improve existing techniques for clustering multi-variate network traffic flow records so that we can quickly infer underlying traffic patterns. In this paper we investigate the use of clustering techniques to identify interesting traffic patterns from network traffic data in an efficient manner. We develop a framework to deal with mixed type attributes including numerical, categorical and hierarchical attributes for a one-pass hierarchical clustering algorithm. We demonstrate the improved accuracy and efficiency of our approach in comparison to previous work on clustering network traffic.