Paper Info
Title
Capturing the real influencing factors of traffic for accurate traffic identification
Abstract
In this paper we introduce a novel framework for traffic identification that employs machine learning techniques focusing on the estimation of multiple traffic influencing factors. The effect of these factors is handled with the training of several machine learning models. We utilize the outcome of the multiple models via a recombination algorithm to achieve high overall true positive and true negative and low overall false positive and false negative classification ratio. The proposed method can improve the performance of every kind of machine learning based traffic identification engine making them capable of efficient operation in changing network environment i.e., when the probing node is trained and tested in different sites.
Year
DOI
Venue
2012
10.1109/ICC.2012.6363978
Communications
Keywords
Field
DocType
Internet,learning (artificial intelligence),pattern classification,telecommunication traffic,Internet service providers,false negative classification ratio,false positive classification ratio,machine learning techniques,multiple traffic influencing factor estimation,probing node,recombination algorithm,traffic identification engine,machine learning,packet header,traffic classification
Traffic classification,Data mining,Active learning (machine learning),Computer science,Computer network,Artificial intelligence,Header,The Internet,Online machine learning,Traffic generation model,Traffic identification,Machine learning,Multiple Models
Conference
ISSN
ISBN
Citations 
1550-3607 E-ISBN : 978-1-4577-2051-2
978-1-4577-2051-2
1
PageRank 
References 
Authors
0.35
7
7
Name
Order
Citations
PageRank
Géza Szabó1958.87
János Szüle2161.87
Bruno Lins310.35
Zoltán Turányi4517.43
Gergely Pongrácz56816.25
Djamel Sadok637157.81
Stenio F. L. Fernandes7707.42