Single Pattern Matching Algorithm Research Articles

Multi pattern matching algorithm for embedded computer network engineering intrusion detection system

In computer networks, security issues persist, and addressing hidden security risks is pivotal for ensuring network security. However, traditional single pattern matching algorithms like BM (Boyer-Moore) lack efficiency for network intrusion detection. This study employs multiple pattern matching algorithms to bolster the security of computer network engineering intrusion detection systems (IDS). A computer network intrusion detection system (NIDS) is designed using embedded technology to collect network logs and other pertinent data, subsequently comparing log data packets. The study delves into a multi pattern matching algorithm, AC (Aho-Corasick), which incorporates the SUNDAY algorithm to optimize unnecessary string matching jumps. Furthermore, the AC algorithm and BM algorithm are fused as control methods. Randomly generated 48M text data is utilized for testing purposes, comparing the AC algorithm, AC-BM algorithm, and AC-SUNDAY algorithm. For instance, when the pattern string length is 20 bytes, the memory consumption of the AC algorithm, AC-BM algorithm, and AC-SUNDAY algorithm is 12.2 MB, 9.8 MB, and 6.2 MB respectively. The findings indicate that applying the AC-SUNDAY algorithm in NIDS effectively reduces memory consumption and enhances the efficacy of network intrusion detection.

Quantum-effective exact multiple patterns matching algorithms for biological sequences.

This article presents efficient quantum solutions for exact multiple pattern matching to process the biological sequences. The classical solution takes Ο(mN) time for matching m patterns over N sized text database. The quantum search mechanism is a core for pattern matching, as this reduces time complexity and achieves computational speedup. Few quantum methods are available for multiple pattern matching, which executes search oracle for each pattern in successive iterations. Such solutions are likely acceptable because of classical equivalent quantum designs. However, these methods are constrained with the inclusion of multiplicative factor m in their complexities. An optimal quantum design is to execute multiple search oracle in parallel on the quantum processing unit with a single-core that completely removes the multiplicative factor m, however, this method is impractical to design. We have no effective quantum solutions to process multiple patterns at present. Therefore, we propose quantum algorithms using quantum processing unit with C quantum cores working on shared quantum memory. This quantum parallel design would be effective for searching all t exact occurrences of each pattern. To our knowledge, no attempts have been made to design multiple pattern matching algorithms on quantum multicore processor. Thus, some quantum remarkable exact single pattern matching algorithms are enhanced here with their equivalent versions, namely enhanced quantum memory processing based exact algorithm and enhanced quantum-based combined exact algorithm for multiple pattern matching. Our quantum solutions find all t exact occurrences of each pattern inside the biological sequence in n}{}O((m/C)sqrt{N}) and n}{}O((m/C)sqrt{t}) time complexities. This article shows the hybrid simulation of quantum algorithms to validate quantum solutions. Our theoretical–experimental results justify the significant improvements that these algorithms outperform over the existing classical solutions and are proven effective in quantum counterparts.

GenSeq+: A Scalable High-Performance Accelerator for Genome Sequencing.

Genome sequencing is one of the most challenging problems in computational biology and bioinformatics. As a traditional algorithm, the string match meets a challenge with the development of the massive volume of data because of gene sequencing. Surveys show that there will be a huge amount of short read segments during the process of gene sequencing and the need for a highly efficient is urgent. As a classic fast and exact single pattern matching algorithm, Knuth-Morris-Pratt (KMP) algorithm has been demonstrated in network security and computational biology. However, with the increasing amount of data in the modern society, it becomes increasingly important and essential to provide a High-performance implementation of KMP algorithm. In this article, we implement a scalable KMP accelerator based on FPGA, named GeneKMP. The accelerator is composed of different computing units to achieve a pipelined organization for higher throughput with satisfying scalability. A novel programming model is provided to alleviate the burden of the high-level programmers. We provide a greedy-based partitioning algorithm for the software/hardware design paradigms. Experimental results on the state-of-the-art Xilinx FPGA hardware prototype show that our accelerator can achieve up to a promising speedup with insignificant hardware cost and power consumption.

Research on an Single Pattern Matching Algorithm

The design of exact single pattern string matching algorithm with high performance is the basis of all string matching problems. To overcome the defects of low efficiency of pattern matching, this paper improves one of the fastest exact single pattern matching algorithms known on English text, which is SBNDM2。The simplest form of the BNDM core loop is obtained, in which there are only 5 instructions per-character read by amending the relationship between position in the pattern and bit in the bit mask. And a cross-border protection method is added to the algorithm in order to reduce the cost of cross-border inspection. Two algorithms named S2BNDM and S2BNDM′ are presented. The experimental results indicate that both S2BNDM and S2BNDM′are faster than SBNDM2 in any case.

The factors analysis and algorithm implementation of single-pattern matching

By studying the algorithms of single pattern matching, five factors that have effect on time complexity of the algorithm are analyzed. The five factors are: sorting the characters of pattern string in an increasing order of using frequency, utilizing already-matched pattern suffix information, utilizing already-matched pattern prefix information, utilizing the position factor which is absorbed from quick search algorithm, and utilizing the continue-skip idea which is originally proposed by this paper. Combining all the five factors, a new single pattern matching algorithm is implemented. It’s proven by the experiment that the efficiency of new algorithm is the best of all algorithms.