Locally testable languages

Zalcstein, Yechezkel

doi:10.1016/s0022-0000(72)80020-5

Cited by 92 publications

(29 citation statements)

References 5 publications

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“…Thus, we focus on a set of languages identifiable in the limit from positive examples in this paper. A subclass of regular tree languages -k-testable tree languages [35] -is identifiable in the limit from positive examples only. These languages are defined in terms of a finite set of k-level-deep tree patterns.…”

Section: Related Workmentioning

confidence: 99%

“…We found that a class of tree languages, namely k-testable tree languages [35] can be inferred directly from dependency graphs, avoiding the expansion to trees. -We improve upon the prior work on inference of k-testable tree languages by providing an O (kN) algorithm, where k is the size of the pattern and N is the size of the graph used for inference.…”

Section: Introductionmentioning

confidence: 99%

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Malware Analysis with Tree Automata Inference

Babić

Reynaud

Song

2011

Lecture Notes in Computer Science

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Abstract. The underground malware-based economy is flourishing and it is evident that the classical ad-hoc signature detection methods are becoming insufficient. Malware authors seem to share some source code and malware samples often feature similar behaviors, but such commonalities are difficult to detect with signature-based methods because of an increasing use of numerous freelyavailable randomized obfuscation tools. To address this problem, the security community is actively researching behavioral detection methods that commonly attempt to understand and differentiate how malware behaves, as opposed to just detecting syntactic patterns. We continue that line of research in this paper and explore how formal methods and tools of the verification trade could be used for malware detection and analysis. We propose a new approach to learning and generalizing from observed malware behaviors based on tree automata inference. In particular, we develop an algorithm for inferring k-testable tree automata from system call dataflow dependency graphs and discuss the use of inferred automata in malware recognition and classification.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Malware Analysis with Tree Automata Inference

Babić

Reynaud

Song

2011

Lecture Notes in Computer Science

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“…The foliowing évidence supports this statement: (1*) L e p 2 if, and only if, S L is finite and e S L e = e [6,18,25]. (2*) L e p 3 if, and only if, S L is finite and e S L e is idempotent and commutative [6,13,25,26,27].…”

Section: Syntactïc Semigroups and Monoidsmentioning

confidence: 74%

Hierarchies of aperiodic languages

Brzozowski¹

1976

R.A.I.R.O. Informatique théorique

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“…They have been extensively used in natural language modeling and also in some music tasks [4]. k-gram models can be regarded as a probabilistic extension of locally testable languages [13]. Informally, a string language L is locally testable if every string w can be recognized as a string in L just by looking at all the substrings in w of length at most k, together with prefixes and suffixes of length strictly smaller than k to check near the string boundaries.…”

Section: Stochastic K-testable Tree Modelsmentioning

confidence: 99%

Classifying Melodies Using Tree Grammars

Bernabeu

Calera-Rubio

Iñesta

2011

Pattern Recognition and Image Analysis

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Abstract. Similarity computation is a difficult issue in music information retrieval, because it tries to emulate the special ability that humans show for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labeling has proven to be effective in melodic similarity computation. In this paper we propose a solution when we have melodies represented by trees for the training but the duration information is not available for the input data. For that, we infer a probabilistic context-free grammar using the information in the trees (duration and pitch) and classify new melodies represented by strings using only the pitch. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and efficient for scalability issues.

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Locally testable languages

Cited by 92 publications

References 5 publications

Malware Analysis with Tree Automata Inference

Malware Analysis with Tree Automata Inference

Hierarchies of aperiodic languages

Classifying Melodies Using Tree Grammars

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