NDLI: Peer-to-Peer Traffic Identification by Mining IP Layer Data Streams Using Concept-Adapting Very Fast Decision Tree

Content Provider	IEEE Xplore Digital Library
Author	Raahemi, B. Weicai Zhong Jing Liu
Copyright Year	2008
Description	Author affiliation: Telfer Sch. of Manage., Univ. of Ottawa, Ottawa, ON (Raahemi, B.; Weicai Zhong) \|\| Inst. of Intell. Inf. Process., Xidian Univ., Xi'an (Jing Liu)
Abstract	We apply streaming data mining techniques, and in particular, concept-adapting very fast decision tree (CVFDT) to identify peer-to-peer (P2P) applications in Internet traffic, as the Internet data flows dynamically in large volumes (streaming data), and in P2P applications, new communities of peers often attend and old communities of peers often leave, requiring the identification methods to be capable of coping with concept drift, and updating the model incrementally. We captured Internet traffic at a main gateway router, performed pre-processing on the captured data, selected the most significant attributes, and prepared a training data stream to which the CVFDT model was applied. We tested our approach on a data stream with 3.5 million P2P and NonP2P traffic records. The results show that our approach can effectively deal with dynamic nature of streaming data and detect the changes in communities of peers. The classification accuracy is higher than 95%, and the method is well-scalable in both time and space complexities, making it competent for large-scale dynamic data. We extracted attributes only from the IP layer, eliminating the privacy concern associated with the techniques that use deep packet inspection.
Starting Page	525
Ending Page	532
File Size	422017
Page Count	8
File Format	PDF
ISBN	9780769534404
ISSN	10823409
DOI	10.1109/ICTAI.2008.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Peer to peer computing Data mining Decision trees Internet Traffic control Training data Testing Large-scale systems Privacy Inspection CVFDT Peer-to-Peer Traffic IP Traffic Identification Stream Data Mining Concept Drift
Content Type	Text
Resource Type	Article

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