Item Details

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

Karode, Andrew (author)

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