Parallel Design and Performance Optimization based on OpenCL Snort

DOI

10.2991/jimec-17.2017.139How to use a DOI?

Keywords

OpenCL snort, GPU, AC algorithm, parallel programming

Abstract

With the rapid increasement of the network speed and number of threats which hide in the network poses enormous challenges to network intrusion detection systems (NIDS). As the most popular NIDS, snort can run as a single threaded application. However, it may not be able to detect intrusions in real-time especially in networks with high traffic. In this paper, a parallel module OpenCL Snort (OCLSnort) is introduced: realize parallel pattern matching algorithm using GPU and innovate new architecture which is more suitable for the parallel algorithm. The result showed that OCLSnort can detect the attacks correctly and effectively, the new system not only has markedly improved on throughput, also effectively reduced the CPU utilization and memory usage.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hongying Xie
AU  - Yangxia Xiang
AU  - Caisen Chen
PY  - 2017/10
DA  - 2017/10
TI  - Parallel Design and Performance Optimization based on OpenCL Snort
BT  - Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC 2017)
PB  - Atlantis Press
SP  - 644
EP  - 647
SN  - 2352-538X
UR  - https://doi.org/10.2991/jimec-17.2017.139
DO  - 10.2991/jimec-17.2017.139
ID  - Xie2017/10
ER  -