Classification of Transport  Layer Data Using Multi-way Association Clustering Analysis
Sheneela Naz, Sohail Asghar, Simon Fong, Amir Qayyum
Journal of Information Security Research, Vol. 2, No. 1, 2011.
DOI: 
Link: http://www.dline.info/jisr/fulltext/v2n1/3.pdf
Abstract:
Currently, the categorization of real time multicast data using payload-based analysis is producing practical limitations with many applications that a network supports. Through this work, we set our goal to identify the recurrent patterns and classification of transport layer data, as an effective measure of anomaly-based intrusion detection. We have identified them by using association rules techniques such as Apriori and clustering algorithms. The evaluation experiment was carried out to test the efficacy of the algorithms. We are able to find an association between flow parameters for network traffic from the simulated data. We advocate that the current study contributes a possible approach of analyzing behavior patterns for building a network traffic intrusion detection system and firewall at Transport layer, by using unsupervised association rule mining and clustering techniques