Efficient Experimental String Matching by Weak Factor Recognition*

Allauzen, Cyril; Crochemore, Maxime; Raffinot, Mathieu

doi:10.1007/3-540-48194-x_5

Cited by 34 publications

(43 citation statements)

References 7 publications

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“…Factor oracles are known to have a number of important properties [9,16]: • They recognize at least all factors of the keyword.…”

Section: Suffix-based Construction Of the Factor Oraclementioning

confidence: 99%

“…These properties ensure that FOs can be represented efficiently compared to exact factor automata and DFAs in general. It should be noted that the above algorithm is O(m 2 ) and that an O(m) construction algorithm for FOs exists, given in [9]. The reasons we focus on the first algorithm here are threefold: this algorithm is much easier to understand, makes most of the mentioned properties obvious, and it formed the basis for our improved FFO construction in Section 4.…”

Section: Suffix-based Construction Of the Factor Oraclementioning

confidence: 99%

“…The factor oracle (FO) (and its variations) provides one particular data structure for efficient factor representation [9]. In fact, the language recognized by an FO constructed for a sequence or string p may recognize slightly more than just the factors of p, but that does not pose a problem per se for the two algorithmic problems mentioned:…”

Section: Introductionmentioning

confidence: 99%

“…the gain in space is traded off for a substantial loss in running time, but partial memoization already leads to drastic runtime improvements. With such partial memoization, the FFOs were also used in a variant of the FO-using backward oracle matching algorithm [9]. We discuss and classify this pattern matching algorithm in terms of the keyword pattern matching taxonomies of Watson, Cleophas, and Zwaan [7,18,19].…”

Section: Introductionmentioning

confidence: 99%

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Weak factor automata: the failure of failure factor oracles?

Cleophas¹,

Kourie²,

Watson³

2014

SACJ

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In indexing of, and pattern matching on, DNA and text sequences, it is often important to represent all factors of a sequence. One efficient, compact representation is the factor oracle (FO). At the same time, any classical deterministic finite automata (DFA) can be transformed to a so-called failure one (FDFA), which may use failure transitions to replace multiple symbol transitions, potentially yielding a more compact representation. We combine the two ideas and directly construct a failure factor oracle (FFO) from a given sequence, in contrast to ex post facto transformation to an FDFA. The algorithm is suitable for both short and long sequences. We empirically compared the resulting FFOs and FOs on number of transitions for many DNA sequences of lengths 4 − 512, showing gains of up to 10% in total number of transitions, with failure transitions also taking up less space than symbol transitions. The resulting FFOs can be used for indexing, as well as in a variant of the FO-using backward oracle matching algorithm. We discuss and classify this pattern matching algorithm in terms of the keyword pattern matching taxonomies of Watson, Cleophas and Zwaan. We also empirically compared the use of FOs and FFOs in such backward reading pattern matching algorithms, using both DNA and natural language (English) data sets. The results indicate that the decrease in pattern matching performance of an algorithm using an FFO instead of an FO may outweigh the gain in representation space by using an FFO instead of an FO.

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“…Factor oracles are known to have a number of important properties [9,16]: • They recognize at least all factors of the keyword.…”

Section: Suffix-based Construction Of the Factor Oraclementioning

confidence: 99%

Section: Suffix-based Construction Of the Factor Oraclementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Weak factor automata: the failure of failure factor oracles?

Cleophas¹,

Kourie²,

Watson³

2014

SACJ

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“…The popular prefix matching algorithms include Aho-Corasick [1], while widely used suffix matching algorithms include SBOM (Set Backward Oracle Matching) [2], Set Horspool [3], and WuManber [4]. Due to an excellent average-case performance, suffix matching algorithms have been widely used.…”

mentioning

confidence: 99%

Attacking Pattern Matching Algorithms Based on the Gap between Average-Case and Worst-Case Complexity

Liu¹,

Liu²,

Li³

et al. 2013

JACN

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Abstract-We have developed an effective method to generate attack data for widely-used pattern matching algorithms. Perceived to be a fundamental problem in the field of computer science, pattern matching has received extensive attention in research and has been used in a variety of fields to include information retrieval, computational biology, information security, etc. The extensive applications of pattern matching are mainly rooted in the fact that pattern matching algorithms, such as SBOM and WuManber, typically have a low time-complexity. However, in the worst case time-complexities of algorithms are very high, making pattern matching algorithms vulnerable to algorithmic complexity attacks (in other words, one might significantly slow down pattern matching algorithms simply by feeding them with specifically-designed text). In this study, we investigated this potential vulnerability by proposing a dynamic programming method designed to attack text having knowledge of patterns. Experimental results suggest that SBOM and WuManber run the specifically-designed text slower than random text (real text). Interestingly, it has been observed that the attacking method is still effective even when parts of patterns are only known, meaning that even a leak of small proportions has the potential to lead to severe attacks. Finally, we propose some suggestions to reduce the risks of these attacks. To the best of our knowledge, this is the first time that an attacking pattern matching approach is proposed based on algorithm complexity.Index Terms-Computer security, algorithmic complexity attacks, pattern matching, SBOM algorithm, WuManber algorithm.

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An efficient parallel algorithm for exact multi‐pattern matching

Zhang

Tian

et al. 2014

Security Comm Networks

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This paper presents a parallel algorithm Parallel Extended Bloom Filter (PEBF) for exact multi‐pattern matching based on Bloom filter. To improve the throughput and parallelism of the algorithm, we divided the pattern set into N subsets where the length of patterns is the same, and different subsets would not intersect each other. We construct an EBF for each subset and use N threads to simultaneously process the subsets in parallel. We implement our solution on the graphics processing unit, called G‐PEBF. Experimental results demonstrate that PEBF performs better than the Wu–Manber (WM) algorithm in terms of time and space. And G‐PEBF outperforms the G‐WM (WM algorithm implemented on graphics processing unit). The speedup of G‐PEBF is up to 60 times at peak performance and almost 10 times at worst performance to the PEBF algorithm. Copyright © 2014 John Wiley & Sons, Ltd.

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Efficient Experimental String Matching by Weak Factor Recognition*

Cited by 34 publications

References 7 publications

Weak factor automata: the failure of failure factor oracles?

Weak factor automata: the failure of failure factor oracles?

Attacking Pattern Matching Algorithms Based on the Gap between Average-Case and Worst-Case Complexity

An efficient parallel algorithm for exact multi‐pattern matching

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