Monitoring Systems White Papers
Offline/Realtime Traffic Classification Using Semi-Supervised Learning
Overview Identifying and categorizing network traffic by application type is challenging because of the continued evolution of applications, especially of those with a desire to be undetectable. The diminished effectiveness of port-based identification and the overheads of deep packet inspection approaches motivate us to classify traffic by exploiting distinctive flow characteristics of applications when they communicate on a network. This paper explores this latter approach and proposes a semi-supervised classification method that can accommodate both known and unknown applications. To the best of one's knowledge, this is the first work to use semi-supervised learning techniques for the traffic classification problem.
| Publisher | Reed Elsevier | File Format | |
|---|---|---|---|
| Date Published | July 2007 | Downloads | 1 |
| Format | White Papers | ||
| Topics | |||
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