NDLI: Rapid Identification of BitTorrent traffic

Content Provider	IEEE Xplore Digital Library
Author	But, J. Branch, P. Tung Le
Copyright Year	2010
Description	Author affiliation: Centre for Advanced Internet Architectures, Swinburne University of Technology, Melbourne, Australia (But, J.; Branch, P.; Tung Le)
Abstract	BitTorrent is one of the dominant traffic generating applications in the Internet today. The ability to identify BitTor-rent traffic in real-time could allow network operators to better manage network traffic and provide a better service to their customers. In this paper we analyse the statistical properties of BitTorrent traffic and select four features that can be used for real-time classification using Machine Learning techniques. We then train and test a classifier using the C4.5 algorithm. Our results show that based on statistics calculated on 150-packet sub-flows, we can classify BitTorrent traffic with Recall of 98.2% and Precision of 96.5%. We then show that 98.1% of sub-flows from other client-server bulk transfer applications are correctly classified as non-BitTorrent.
Starting Page	536
Ending Page	543
File Size	333742
Page Count	8
File Format	PDF
ISBN	9781424483877
ISSN	07421303
e-ISBN	9781424483891
DOI	10.1109/LCN.2010.5735770
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Payloads Protocols Machine learning Real time systems Feature extraction Servers Classification algorithms
Content Type	Text
Resource Type	Article

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	Mr. Siddhartha Mukherjee siddhartha@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	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray 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

Machine Learned Real-Time Traffic Classifiers

An Omnibus Identification of BitTorrent Traffic in a Stub Network

Rapid identification of bittorrent traffic.

Behavior signature for big data traffic identification

Internet Traffic Classification Using Machine Learning: A Token-based Approach

Optimal supervised feature extraction in internet traffic classification

A novel traffic classification algorithm using machine learning

Issues and future directions in traffic classification

SANTaClass: A Self Adaptive Network Traffic Classification system

Rapid Identification of BitTorrent traffic

Similar Documents

Machine Learned Real-Time Traffic Classifiers

An Omnibus Identification of BitTorrent Traffic in a Stub Network

Rapid identification of bittorrent traffic.

Behavior signature for big data traffic identification

Internet Traffic Classification Using Machine Learning: A Token-based Approach

Optimal supervised feature extraction in internet traffic classification

A novel traffic classification algorithm using machine learning

Issues and future directions in traffic classification

SANTaClass: A Self Adaptive Network Traffic Classification system

Rapid Identification of BitTorrent traffic