NDLI: Timely and continuous machine-learning-based classification for interactive IP traffic

Content Provider	ACM Digital Library
Author	Branch, Philip Nguyen, Thuy T. T. Zander, Sebastian Armitage, Grenville
Copyright Year	2013
Abstract	Machine Learning (ML) for classifying IP traffic has relied on the analysis of statistics of full flows or their first few packets only. However, automated QoS management for interactive traffic flows requires quick and timely classification well before the flows finish. Also, interactive flows are often long-lived and should be continuously monitored during their lifetime. We propose to achieve this by using statistics derived from sub-flows--a small number of most recent packets taken at any point in a flow's lifetime. Then, the ML classifier must be trained on a set of sub-flows, and we investigate different sub-flow selection strategies. We also propose to augment training datasets so that classification accuracy is maintained even when a classifier mixes up client-to-server and server-to-client directions for applications exhibiting asymmetric traffic characteristics. We demonstrate the effectiveness of our approach with the Naive Bayes and C4.5 Decision Tree ML algorithms, for the identification of first-person-shooter online game and VoIP traffic. Our results show that we can classify both applications with up to 99% Precision and 95% Recall within less than 1 s. Stable results are achieved regardless of where within a flow the classifier captures the packets and the traffic direction.
Starting Page	1880
Ending Page	1894
Page Count	15
File Format	PDF
ISSN	10636692
DOI	10.1109/TNET.2012.2187305
Volume Number	20
Issue Number	6
Journal	IEEE/ACM Transactions on Networking (TON)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-12-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Interactive traffic Machine learning (ML) Sub-flows Traffic classification
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Networks and Communications Software Computer Science Applications

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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
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