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.
|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|
|Publication date||1 Jul 2012|
|Pages||000882 - 000887|
|Conference||The Seventeenth IEEE Symposium on Computers and Communications|
|Periode||01-07-12 → 04-07-12|
- traffic classification, computer networks, HTTP traffic, browser traffic, C5.0, Machine Learning Algorithms (MLAs), performance monitoring
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