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

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

Peer-to-Peer Networking and Applications : Volume 10

Peer-to-Peer Networking and Applications : Volume 9

Peer-to-Peer Networking and Applications : Volume 8

Peer-to-Peer Networking and Applications : Volume 7

Peer-to-Peer Networking and Applications : Volume 6

Peer-to-Peer Networking and Applications : Volume 6, Issue 4, December 2013

Peer-to-Peer Networking and Applications : Volume 6, Issue 3, September 2013

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

Cost-effective capacity migration of Peer-to-Peer social media to clouds

Collaborative applications over peer-to-peer systems–challenges and solutions

Peer-assisted adaptation in periodic broadcasting of videos for heterogeneous clients

Kademlia based routing on locator-ID separated networks for new generation networks

Cognitive vertical handover engine for vehicular communication

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

Optimizing content placement in a peer-assisted VoD architecture

Peer-to-Peer Networking and Applications : Volume 6, Issue 2, June 2013

Peer-to-Peer Networking and Applications : Volume 6, Issue 1, March 2013

Peer-to-Peer Networking and Applications : Volume 5

Peer-to-Peer Networking and Applications : Volume 4

Peer-to-Peer Networking and Applications : Volume 3

Peer-to-Peer Networking and Applications : Volume 2

Peer-to-Peer Networking and Applications : Volume 1

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

Content Provider	Springer Nature Link
Author	Zhong, Weicai Raahemi, Bijan Liu, Jing
Copyright Year	2012
Abstract	Peer-to-Peer (P2P) applications generate streaming data in large volumes, where new communities of peers regularly attend and existing communities of peers regularly leave, requiring the classification techniques to consider concept drift, and update the model incrementally. Concept-adapting Very Fast Decision Tree (CVFDT) is one of the well-known streaming data mining techniques that can be applied to P2P traffic. However, we observe that P2P traffic data is class imbalanced, namely, only about 30 % of examples can be labeled as “P2P”, biasing the trained models (e.g. decision tree) towards the majority class (i.e. “NonP2P”). In this paper, we propose a new technique, the imbalanced CVFDT (iCVFDT), by integrating the CVFDT with an efficient resampling technique to address the issue of the class imbalanced data. The iCVFDT classification technique enjoys the advantages of CVFDT (such as stability), and at the same time, is not sensitive to imbalanced data. We captured the Internet traffic at a main gateway and prepared a real data stream with 3.5 million examples to which the iCVFDT classification technique was applied. The experimental results demonstrate a significant improvement in the performance of the iCVFDT compared to that of the CVFDT.
Starting Page	233
Ending Page	246
Page Count	14
File Format	PDF
ISSN	19366442
Journal	Peer-to-Peer Networking and Applications
Volume Number	6
Issue Number	3
e-ISSN	19366450
Language	English
Publisher	Springer US
Publisher Date	2012-06-04
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Peer-to-Peer traffic classification Class imbalance problem Concept-adapting decision tree Resampling Stream data mining Communications Engineering, Networks Information Systems and Communication Service Computer Communication Networks Signal, Image and Speech Processing
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

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

Classifying Data Streams with Skewed Class Distributions and Concept Drifts

Methods to Handle Multiclass Imbalance Data in Educational Data Mining

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

Efficient model sharing for scalable collaborative classification

Performance evaluation of Enhanced Very Fast Decision Tree (EVFDT) mechanism for distributed denial-of-service attack detection in health care systems

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

Identifying P2P traffic: A survey

Adapted One-versus-All Decision Trees for Data Stream Classification

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

Similar Documents

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

Classifying Data Streams with Skewed Class Distributions and Concept Drifts

Methods to Handle Multiclass Imbalance Data in Educational Data Mining

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

Efficient model sharing for scalable collaborative classification

Performance evaluation of Enhanced Very Fast Decision Tree (EVFDT) mechanism for distributed denial-of-service attack detection in health care systems

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

Identifying P2P traffic: A survey

Adapted One-versus-All Decision Trees for Data Stream Classification

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