期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：6
DOI：10.15680/ijircce.2015.0306034
出版社：S&S Publications
摘要：Network Intrusion Detection Systems (NIDS) need to handle computationally intensive operations likepattern matching, where huge amount of data needs to be matched against the known patterns. Storage capacity andlink speed has increased with the advent in technology. Therefore there has been an increase in the amount of data thatneeds to be matched against the known patterns. The traditional algorithms cannot handle this increased amount ofdata. Therefore, we need such a hardware and software solution that would help to handle this increased amount ofincoming data in NIDS to match it with the known patterns. We have used a parallel multipattern matching algorithmthat matches an input string with the known patterns (attack patterns or virus signatures) to check for the presence ofany pattern in an input string and would return the state in the DFA and position in an input string where the patternwas found. We have run this algorithm on NVIDIA Geforce GTX 680 GPU with CUDA 6.5 programming model. Wehave also introduced several optimization techniques for the Parallel Aho-Corasick algorithm that would result in thereduction of memory usage, time and cost required to execute Parallel AC algorithm on GPU. Our results show thatOptimized Parallel Aho-Corasick algorithm on GPU takes very less time for execution as compared to running theSerial Aho-Corasick algorithm on CPU, Parallel Aho-Corasick algorithm on CPU and Unoptimized Parallel Aho-Corasick algorithm on GPU.
关键词：Pattern Matching; KMP algorithm; Snort; AC algorithm; GPU; CUDA
