NDLI: Exploiting unlabeled data to improve peer-to-peer traffic classification using incremental tri-training method

Please wait, while we are loading the content...

Exploiting unlabeled data to improve peer-to-peer traffic classification using incremental tri-training method

Content Provider	Springer Nature Link
Author	Raahemi, Bijan Zhong, Weicai Liu, Jing
Copyright Year	2009
Abstract	Unlabeled training examples are readily available in many applications, but labeled examples are fairly expensive to obtain. For instance, in our previous works on classification of peer-to-peer (P2P) Internet traffics, we observed that only about 25% of examples can be labeled as “P2P”or “NonP2P” using a port-based heuristic rule. We also expect that even fewer examples can be labeled in the future as more and more P2P applications use dynamic ports. This fact motivates us to investigate the techniques which enhance the accuracy of P2P traffic classification by exploiting the unlabeled examples. In addition, the Internet data flows dynamically in large volumes (streaming data). In P2P applications, new communities of peers often join and old communities of peers often leave, requiring the classifiers to be capable of updating the model incrementally, and dealing with concept drift. Based on these requirements, this paper proposes an incremental Tri-Training (iTT) algorithm. We tested our approach on a real data stream with 7.2 Mega labeled examples and 20.4 Mega unlabeled examples. The results show that iTT algorithm can enhance accuracy of P2P traffic classification by exploiting unlabeled examples. In addition, it can effectively deal with dynamic nature of streaming data to detect the changes in communities of peers. We extracted attributes only from the IP layer, eliminating the privacy concern associated with the techniques that use deep packet inspection.
Starting Page	87
Ending Page	97
Page Count	11
File Format	PDF
ISSN	19366442
Journal	Peer-to-Peer Networking and Applications
Volume Number	2
Issue Number	2
e-ISSN	19366450
Language	English
Publisher	Springer US
Publisher Date	2009-01-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Stream data mining Concept drift Windowing technique Tri-training Unlabeled data Peer-to-peer traffic IP traffic identification Signal, Image and Speech Processing Computer Communication Networks Information Systems and Communication Service Communications Engineering, Networks
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Classifying peer-to-peer applications using imbalanced concept-adapting very fast decision tree on IP data stream

Identifying P2P traffic: A survey

File-aware P2P traffic classification: An aid to network management

Efficient model sharing for scalable collaborative classification

Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks

Automated Dataset Generation for Training Peer-to-Peer Machine Learning Classifiers

Research on detection and integration classification based on concept drift of data stream

One-class classifiers with incremental learning and forgetting for data streams with concept drift

Tri-training: exploiting unlabeled data using three classifiers

Exploiting unlabeled data to improve peer-to-peer traffic classification using incremental tri-training method

Similar Documents

Classifying peer-to-peer applications using imbalanced concept-adapting very fast decision tree on IP data stream

Identifying P2P traffic: A survey

File-aware P2P traffic classification: An aid to network management

Efficient model sharing for scalable collaborative classification

Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks

Automated Dataset Generation for Training Peer-to-Peer Machine Learning Classifiers

Research on detection and integration classification based on concept drift of data stream

One-class classifiers with incremental learning and forgetting for data streams with concept drift

Tri-training: exploiting unlabeled data using three classifiers

Exploiting unlabeled data to improve peer-to-peer traffic classification using incremental tri-training method