Paper Info
Title
Online Classification of Network Flows
Abstract
Summary form only given. Online classification of network traffic is very challenging and still an issue to be solved due to the increase of new applications and traffic encryption. In this paper, we propose a hybrid mechanism for online classification of network traffic, in which we apply a signature-based method at the first level, and then we take advantage of a learning algorithm to classify the remaining unknown traffic using statistical features. Our evaluation with over 250 thousand flows collected over three consecutive hours on a large-scale ISP network shows promising results in detecting encrypted and tunneled applications compared to other existing methods.
Year
DOI
Venue
2009
10.1109/CNSR.2009.22
Moncton, NB
Keywords
Field
DocType
new application,consecutive hour,existing method,hybrid mechanism,network flows,large-scale isp network,remaining unknown traffic,network traffic,online classification,traffic encryption,signature-based method,cryptography,learning artificial intelligence,computer science,protocols,application software,digital signatures,communication networks,decision trees,traffic classification,network flow,classification algorithms,data mining,accuracy,payloads,niobium,statistical analysis
Traffic classification,Decision tree,Data mining,Cryptography,Computer science,Computer network,Digital signature,Encryption,Artificial intelligence,Flow network,Traffic generation model,Statistical classification,Machine learning
Conference
ISBN
Citations 
PageRank 
978-0-7695-3649-1
5
0.66
References 
Authors
32
3
Name
Order
Citations
PageRank
Mahbod Tavallaee174829.01
Wei Lu270330.81
Ali A. Ghorbani31891135.01