Graph Grammar Induction on Structural Data for Visual Programming

Ates, K.; Kukluk, Jacek P.; Holder, Lawrence B.; Cook, Diane J.; Zhang, Kang

doi:10.1109/ictai.2006.61

Cited by 16 publications

(16 citation statements)

References 13 publications

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“…The inference performed is similar to the aforementioned proposed by Ates et al [9] because they both use the Spatial Graph Grammar (SGG) formalism and subgraph compression. The induction algorithm consumes webpages, or more accurately their DOM trees, to produce a graph grammar.…”

Section: Inference Of Graph Grammars and Visual Languagesmentioning

confidence: 91%

“…Another graph grammar inference algorithm is proposed by Ates et al which repeatedly finds and compresses overlapping identical subgraphs to a single nonterminal node [9]. This system uses only positive samples during the inference process, but validates the resulting grammar by ensuring all the graphs in the training set are parsable and other graphs which are close to but distinct from the training graphs are not parsable.…”

Section: Inference Of Graph Grammars and Visual Languagesmentioning

confidence: 99%

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Grammatical Inference in Software Engineering: An Overview of the State of the Art

Stevenson

Cordy

2013

Software Language Engineering

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Abstract. Grammatical inference -used successfully in a variety of fields such as pattern recognition, computational biology and natural language processing -is the process of automatically inferring a grammar by examining the sentences of an unknown language. Software engineering can also benefit from grammatical inference. Unlike the aforementioned fields, which use grammars as a convenient tool to model naturally occuring patterns, software engineering treats grammars as first-class objects typically created and maintained for a specific purpose by human designers. We introduce the theory of grammatical inference and review the state of the art as it relates to software engineering.

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Section: Inference Of Graph Grammars and Visual Languagesmentioning

confidence: 91%

Section: Inference Of Graph Grammars and Visual Languagesmentioning

confidence: 99%

Grammatical Inference in Software Engineering: An Overview of the State of the Art

Stevenson

Cordy

2013

Software Language Engineering

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“…This is achieved by mining recurring behavioral patterns from execution traces using VEGGIE with SubdueGL [4,5], a Minimum Description Length (MDL)-based compression algorithm. The inferred graph grammar and a syntactic parse tree visually represent reused structures found.…”

Section: Related Workmentioning

confidence: 99%

“…Overgeneralization occurs when the inference process produces a grammar whose language is larger than the unknown language. The use of negligible items results in an unnecessarily evolutionary GA-based [42] L A g t s [ 8] heuristic ALLiS [13] Inductive CYK [36] ABL [54] MDL e-GRIDS [38] CDC [10] VEGGIE [4,5] Eiland et al [17] greedy search ADIOS CDC Incremental parsing [3,44] Sequitur [37] GraphViz [45,46] clustering EMILE [1] C D C large grammar. To limit the impact of over-generalization, it is recommended to also use a set of negative examples.…”

Section: Grammar Inferencementioning

confidence: 99%

SEQUIN: a grammar inference framework for analyzing malicious system behavior

Luh

Schramm

Wagner

et al. 2018

J Comput Virol Hack Tech

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Targeted attacks on IT systems are a rising threat to the confidentiality of sensitive data and the availability of critical systems. The emergence of Advanced Persistent Threats (APTs) made it paramount to fully understand the particulars of such attacks in order to improve or devise effective defense mechanisms. Grammar inference paired with visual analytics (VA) techniques offers a powerful foundation for the automated extraction of behavioral patterns from sequential event traces. To facilitate the interpretation and analysis of APTs, we present SEQUIN, a grammar inference system based on the Sequitur compression algorithm that constructs a context-free grammar (CFG) from string-based input data. In addition to recursive rule extraction, we expanded the procedure through automated assessment routines capable of dealing with multiple input sources and types. This automated assessment enables the accurate identification of interesting frequent or anomalous patterns in sequential corpora of arbitrary quantity and origin. On the formal side, we extended the CFG with attributes that help describe the extracted (malicious) actions. Discovery-focused pattern visualization of the output is provided by our dedicated KAMAS VA prototype.

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“…We adopt VEGGIE [1][2], a Visual Environment for Graph Grammars: Induction and Engineering, to infer graph grammars from program execution traces. VEGGIE essentially incorporates two subsystems: SubdueGL [6] [7] and SGG [8] [9].…”

Section: Introductionmentioning

confidence: 99%