Implementation of Multipattern String Matching Accelerated with GPU for Intrusion Detection System

Abstract. As Internet-related  security threats continue to increase  in terms of volume and sophistication,  existing  Intrusion Detection System  is also  being challenged to cope with the current Internet  development. Multi Pattern String Matching algorithm accelerated with Graphical  Processing Unit  is  being  utilized  to improve  the packet scanning performance  of the  IDS. This paper implements a Multi Pattern String Matching algorithm, also called Parallel Failureless Aho  Corasick  accelerated with  GPU to  improve the  performance of IDS.  OpenCL library  is  used  to  allow  the  IDS  to  support  various GPU, including  popular GPU such  as NVIDIA and AMD, used  in our research. The  experiment result  shows that the application of Multi  Pattern String Matching using GPU  accelerated platform provides  a speed up,  by up  to 141% in term of throughput compared to the  previous  research

A New Multi-threaded and Interleaving Approach to Enhance String Matching for Intrusion Detection Systems

String matching algorithms are computationally intensive operations in computer science. The algorithms find the occurrences of one or more strings patterns in a larger string or text. String matching algorithms are important for network security, biomedical applications, Web search, and social networks. Nowadays, the high network speeds and large storage capacity put a high requirement on string matching methods to perform the task in a short time. Traditionally, Aho-Corasick algorithm, which is used to find the string matches, is executed sequentially. In this paper, a new multi-threaded and interleaving approach of Aho-Corasick using graphics processing units (GPUs) is designed and implemented to achieve high-speed string matching. Compute Unified Device Architecture (CUDA) programming language is used to implement the proposed parallel version. Experimental results show that our approach achieves more than 5X speedup over the sequential and other parallel implementations. Hence, a wide range of applications can benefit from our solution to perform string matching faster than ever before

High Performance Pattern Matching on Heterogeneous Platform

Pattern discovery is one of the fundamental tasks in bioinformatics and pattern recognition is a powerful technique for searching sequence patterns in the biological sequence databases. Fast and high performance algorithms are highly demanded in many applications in bioinformatics and computational molecular biology since the significant increase in the number of DNA and protein sequences expand the need for raising the performance of pattern matching algorithms. For this purpose, heterogeneous architectures can be a good choice due to their potential for high performance and energy efficiency. In this paper we present an efficient implementation of Aho-Corasick (AC) which is a well known exact pattern matching algorithm with linear complexity, and Parallel Failureless Aho-Corasick (PFAC) algorithm which is the massively parallelized version of AC algorithm without failure transitions, on a heterogeneous CPU/GPU architecture. We progressively redesigned the algorithms and data structures to fit on the GPU architecture. Our results on different protein sequence data sets show that the new implementation runs 15 times faster compared to the original implementation of the PFAC algorithm