Learning in the presence of concept drift and hidden contexts
Machine Learning
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
Comparison of Software Product Line Architecture Design Methods: COPA, FAST, FORM, KobrA and QADA
Proceedings of the 26th International Conference on Software Engineering
estWin: Online data stream mining of recent frequent itemsets by sliding window method
Journal of Information Science
Profiling internet backbone traffic: behavior models and applications
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
A Framework for On-Demand Classification of Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
Internet Measurement: Infrastructure, Traffic and Applications
Internet Measurement: Infrastructure, Traffic and Applications
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
WIT: A wireless integrated traffic model
Mobile Information Systems - Information Assurance and Advanced Human-Computer Interfaces
Mobile Information Systems - Advances in Wireless Networks
Traffic classification using a statistical approach
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Self-Learning IP traffic classification based on statistical flow characteristics
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams
International Journal of Data Warehousing and Mining
Using multi decision tree technique to improving decision tree classifier
International Journal of Business Intelligence and Data Mining
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This paper presents a new approach using data mining techniques, and in particular a two-stage architecture, for classification of Peer-to-Peer P2P traffic in IP networks where in the first stage the traffic is filtered using standard port numbers and layer 4 port matching to label well-known P2P and NonP2P traffic. The labeled traffic produced in the first stage is used to train a Fast Decision Tree FDT classifier with high accuracy. The Unknown traffic is then applied to the FDT model which classifies the traffic into P2P and NonP2P with high accuracy. The two-stage architecture not only classifies well-known P2P applications, but also classifies applications that use random or non-standard port numbers and cannot be classified otherwise. The authors captured the internet traffic at a gateway router, performed pre-processing on the data, selected the most significant attributes, and prepared a training data set to which the new algorithm was applied. Finally, the authors built several models using a combination of various attribute sets for different ratios of P2P to NonP2P traffic in the training data.