Classification of HTTP traffic based on C5.0 Machine Learning Algorithm
Publication: Research - peer-review › Article in proceeding
Our previous work demonstrated the possibility of distinguishing several kinds of applications with accuracy of over 99%. Today, most of the traffic is generated by web browsers, which provide different kinds of services based on the HTTP protocol: web browsing, file downloads, audio and voice streaming through third-party plugins, etc. This paper suggests and evaluates two approaches to distinguish various HTTP content: distributed among volunteers' machines and centralized running in the core of the network. We also assess accuracy of the global classifier for both HTTP and non-HTTP traffic. We achieved accuracy of 94%, which supposed to be even higher in real-life usage. Finally, we provided graphical characteristics of different kinds of HTTP traffic.
| Original language | English |
|---|---|
| Title | Fourth IEEE International Workshop on Performance Evaluation of Communications in Distributed Systems and Web based Service Architectures (PEDISWESA'12) |
| Number of pages | 6 |
| Place of publication | Cappadocia |
| Publisher | IEEE Press |
| Publication date | 1 Jul 2012 |
| Pages | 000882 - 000887 |
| ISBN (print) | 978-1-4673-2712-1 |
| ISBN (electronic) | 978-1-4673-2711-4 |
| DOIs | |
| State | Published |
Conference
| Conference | The Seventeenth IEEE Symposium on Computers and Communications |
|---|---|
| Land | Turkey |
| By | Cappadocia |
| Periode | 01-07-12 → 04-07-12 |
Keywords
- traffic classification, computer networks, HTTP traffic, browser traffic, C5.0, Machine Learning Algorithms (MLAs), performance monitoring
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ID: 62073457