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GPU packet classification using OpenCL: a consideration of viable classification methods

Alastair Nottingham, Barry Irwin
Department of Computer Science, Rhodes University, Grahamstown, South Africa
In SAICSIT ’09: Proceedings of the 2009 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists (2009), pp. 160-169.

@conference{nottingham2009gpu,

   title={GPU packet classification using OpenCL: a consideration of viable classification methods},

   author={Nottingham, A. and Irwin, B.},

   booktitle={Proceedings of the 2009 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists},

   pages={160–169},

   year={2009},

   organization={ACM}

}

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Packet analysis is an important aspect of network security, which typically relies on a flexible packet filtering system to extrapolate important packet information from each processed packet. Packet analysis is a computationally intensive, highly parallelisable task, and as such, classification of large packet sets, such as those collected by a network telescope, can require significant processing time. We wish to improve upon this, through parallel classification on a GPU. In this paper, we first consider the OpenCL architecture and its applicability to packet analysis. We then introduce a number of packet demultiplexing and routing algorithms, and finally present a discussion on how some of these techniques may be leveraged within a GPGPU context to improve packet classification speeds.
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