A Parallel Algorithm of Multiple String Matching Based on Set-Partition in Multi-core Architecture

Liu, Jiahui; Li, Fangzhou; Sun, Guanglu

doi:10.14257/ijsia.2016.10.4.25

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…The main idea behind the parallel processing is to divide a heavy task into sub-tasks that can be solved simultaneously through using multi-core computers or more than one thread to enhance the overall execution time of the computations. In addition to the importance of parallel string matching algorithms in many areas such as biological applications [2], search engines applications [19], intrusion detection in network applications [1][11] and many other applications, several parallel string matching algorithms have been proposed in different platforms such as Graphics Processing Unit (GPU) [14][18], Field-Programmable Gate Array (FPGA) [15] and a multi-core architecture with message passing interface [16] [17].…”

Section: Related Workmentioning

confidence: 99%

Parallel Enhanced Pattern Matching Algorithm with Two Sliding Windows PETSW

Dababat¹,

Itriq²

2018

IJCA

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String matching problem is one of the most essential problems in many computer science fields, such as DNA analysis, artificial intelligence, internet search engines and information retrieval. Today, the speed and performance of string matching algorithms is critical and must be improved to meet recent developments in hardware processing environments. The improvement in performance gained by the use of a multi core processor depends very much on the software algorithms used and their implementation. However, the most important factor when writing a parallel algorithm is the fraction of the algorithm that can run simultaneously on multiple cores. In this paper, an efficient algorithm for string matching, Enhanced Pattern Matching Algorithm with Two Sliding Windows (ETWS), is adapted to be implemented under a real parallel environment (PETWS), to enhance the performance of the sequential algorithm through providing less execution time to make it more suitable for today's applications.

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Section: Related Workmentioning

confidence: 99%

Parallel Enhanced Pattern Matching Algorithm with Two Sliding Windows PETSW

Dababat¹,

Itriq²

2018

IJCA

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“…On the one hand, when l > s, a Greedy algorithm is applied to scheduling subsets into multicores, but it is easily trapped in the local optimization. A set-partition based genetic algorithm (GA) [13] is proposed to resolve the problem, but it is still easy to fall premature. On the other hand, if some subsets cost too much runtime, according to the strategy by Tan et al, there will be a phenomenon min max =1 { } = max =1 { }; this means that the whole runtime always depends on a subset.…”

Section: Related Workmentioning

confidence: 99%

“…The result shows that the relation is linear; that is, the running time for WM and SBOM increases with the number increase, so we divide subset according to the ratio of time FOR each individual DO (4) calculate the fitness (5) ENDFOR (6) IF ≥ , THEN 7find the optimal set-partition. (8) return (9) ENDIF (10) calculate , , (11) IF ≥ , THEN (12) calculate , (13) crossover operation with probability (14) mutation operation with probability ( find core with max =1 { }, core j with min =1 { } (4) find the subset ℎ max (5) split = { 1 , 2 }, move 2 to core j (6) recalculate , (7) GOTO 1 (8) ELSE (9) find the optimal set-partition, return (10) ENDIF Algorithm 2: Greedy algorithm to split subsets.…”

Section: Optimal Patterns Set Decomposition and Schedulementioning

confidence: 99%

An Efficient Security System for Mobile Data Monitoring

Liu

Zhang

et al. 2018

Wireless Communications and Mobile Computing

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During the last decade, rapid development of mobile devices and applications has produced a large number of mobile data which hide numerous cyber-attacks. To monitor the mobile data and detect the attacks, NIDS/NIPS plays important role for ISP and enterprise, but now it still faces two challenges, high performance for super large patterns and detection of the latest attacks. High performance is dominated by Deep Packet Inspection (DPI) mechanism, which is the core of security devices. A new TTL attack is just put forward to escape detecting, such that the adversary inserts packet with short TTL to escape from NIDS/NIPS. To address the above-mentioned problems, in this paper, we design a security system to handle the two aspects. For efficient DPI, a new two-step partition of pattern set is demonstrated and discussed, which includes first set-partition and second set-partition. For resisting TTL attacks, we set reasonable TTL threshold and patch TCP protocol stack to detect the attack. Compared with recent produced algorithm, our experiments show better performance and the throughput increased 27% when the number of patterns is 106. Moreover, the success rate of detection is 100%, and while attack intensity increased, the throughput decreased.

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A Parallel Algorithm of Multiple String Matching Based on Set-Partition in Multi-core Architecture

Abstract: Abstract

Cited by 2 publications

References 27 publications

Parallel Enhanced Pattern Matching Algorithm with Two Sliding Windows PETSW

Parallel Enhanced Pattern Matching Algorithm with Two Sliding Windows PETSW

An Efficient Security System for Mobile Data Monitoring

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