Support Vector Machine Classification of Network Streams Using a Spectrum Kernel Encoding
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- abstract
- The growth of computer networking has raised the profile of network management issues, and for those institutions (academic, private, and public) that require high-speed networks these issues have not only become more pressing, but also more challenging. In particular, performance and security management rely heavily on automated tools that must operate in real-time, but creating real-time tools can be a difficult problem that is only exacerbated by prevalence of high-speed networking. One important piece of these tools is the task of network stream classification, which allows for the application protocol of a stream to be identified. Traditional stream classification methods, however, are becoming less reliable, due to increased use of both non-standard ports and encryption algorithms. As such, this work proposes a novel method for network stream classification, relying on the Support Vector Machine (SVM) algorithm. Using only information available in the headers of TCP packets, the SVM creates temporal features – encoded using a spectrum kernel representation – and aggregate features for classification. Experimental results show that 6 protocols of interest are classified with over 99% accuracy. Also, 200, 000 streams can be classified in, on average, 148 seconds, with a strong promise of further speed increases due to parallelization.
- subject
- spectrum kernel
- support vector machines
- network stream classification
- contributor
- William H. Turkett, Jr. (committee chair)
- David J. John (committee member)
- Errin W. Fulp (committee member)
- date
- 2009-01-27T19:21:53Z (accessioned)
- 2010-06-18T18:59:05Z (accessioned)
- 2009-01-27T19:21:53Z (available)
- 2010-06-18T18:59:05Z (available)
- 2009-01-27T19:21:53Z (issued)
- degree
- Computer Science (discipline)
- identifier
- http://hdl.handle.net/10339/14826 (uri)
- language
- en_US (iso)
- publisher
- Wake Forest University
- rights
- Release the entire work immediately for access worldwide. (accessRights)
- title
- Support Vector Machine Classification of Network Streams Using a Spectrum Kernel Encoding
- type
- Thesis