Accelaration Of Preprocessors Of The Snort Network Intrusion Detection System Using General Purpose Graphics Processing Unit

Advances in networking technologies enable interactions and communications atrnhigh speeds and large data volumes. But, securing data and the infrastructurernhas become a big issue. Intrusion Detection Systems such as Snort play an importantrnrole to secure the network. Intrusion detection systems are used to monitorrnnetworks for unauthorized access. Snort has a packet decoder, pre-processor, detectionrnengine and an alerting system. The detection engine is the most computernintensive part followed by the pre-processor. Previous work has shown how generalrnpurpose graphics processing units(GP-GPU) can be used to accellerate therndetection engine. This work focused on the pre-processors of Snort, speci cally,rnthe stream5 pre-processor as pro ling revealed it to be the most time consumingrnof the pre-processors. The analysis shows that the individual implementation ofrnstream5 using Compute Uni ed Device Architecture(CUDA) achieved up to verntimes speed up over the baseline. Also, an over all 15.5 percent speed up on thernDefense Advanced Research Projects Agency(DARPA) intrusion detection systemrndataset was observed when integrated in Snort.rnKey words: Intrusion Detection System, Snort, Graphics Processing Unit,rnCUDA, Parallelization, Porting, Preprocessor.

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