Advanced Grammar-Based Fuzzing

Sargsyan, Sevak; Hakobyan, Jivan; Mehrabyan, Matevos; Mkoyan, Ruslan; Sahakyan, Vardan; Melkonyan, Vahagn; Arutunian, Mariam; Fahradyan, Arman; Avetisyan, Arutyun

doi:10.1109/ivmem57067.2022.9983953

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…A proposal closely aligned with our on-ongoing research has been proposed by Sargsyan et al [14] who demonstrated the capabilities of utilizing ANTLR4's automata for code generation of over 120 languages (EBNF grammars). Their proposed prototype uses random selection to determine what paths to traverse across the generation process.…”

Section: B Grammar Assisted Sentence Generationmentioning

confidence: 95%

“…This type of method was highlighted further by Gao et al [28] using machine learning techniques and the same Seq2Seq approach to automatically generate protocols such as FTP. Their implementation uses an initial grammar-based fuzzer [14] to obtain input data which is then transformed as features for the model.…”

Section: Ai-driven Sentence Generationmentioning

confidence: 99%

“…It is worth indicating that the only comparable results available in the state of the art against our solution [14] do not have any available open source to be used for comparison. It has not allowed us to establish a baseline metric for evaluation between tools.…”

Section: B Experimental Validation 1) Sql Generationmentioning

confidence: 99%

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Enabling Generative AI to Produce SQL Statements: A Framework for the Auto- Generation of Knowledge Based on EBNF Context-Free Grammars

Troy,

Sturley,

Alcaraz-Calero

et al. 2023

IEEE Access

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The motivation of this paper is to be able to generate high-quality (Structured Query Language) SQL language sentences in terms of syntax and semantics so that they are intended to achieve a concrete predefined and well-known aim. For example, generating SQL-sentences that are capable of detecting a cyber-attack from a set of metrics available in a database table. Two solutions are needed to achieve so, a tool that enables and performs the syntactically valid generation of SQL sentences and an (Artificial intelligence) AI algorithm able to guide the semantics of such generations to the achievement of the best sentences for the intended purpose. The main contribution of this manuscript is the first of these solutions. To be concrete, this paper proposes a tool to enable and generate syntactic-valid language sentences. The tool can deal with any language defined as an ANTLR4 EBNF (Extended Backus-Naur Form) grammar. The paper also provides a methodology to help achieve an EBNF grammar suitable for addressing concerns related to ambiguity and recursion as a direct result of the generation process. The paper further implements a prototype utilizing ANTLR4's recognizer and its Augmented Transition Network for language generation using EBNF grammars. In-depth design and logic implementation are provided, showcasing areas of interest for AI integration. The achieved prototype showed an ability to easily generate syntactically valid SQL statements at various depths, with observable problems becoming more apparent during the exponential recursive growth. Our mitigation controls for such scenarios proved to be successful and were able to complete the recursion whilst also moving the push-down automata forward until query completion. Experimental validation was performed against a SQL EBNF grammar feeding the generated SQL statement into an SQL parser to validate the syntax.

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Section: B Grammar Assisted Sentence Generationmentioning

confidence: 95%

Section: Ai-driven Sentence Generationmentioning

confidence: 99%

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Enabling Generative AI to Produce SQL Statements: A Framework for the Auto- Generation of Knowledge Based on EBNF Context-Free Grammars

Troy,

Sturley,

Alcaraz-Calero

et al. 2023

IEEE Access

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“…In this context, designing an automated and universal method for extracting network protocol syntax information has become an important and challenging research issue. The designated method not only needs to improve the efficiency of protocol syntax information extraction, but also should maintain high versatility and accuracy to ensure that it is applicable to a wider range of protocols [22,23].…”

Section: Introductionmentioning

confidence: 99%

A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing

Li,

Zhang,

Zhao

et al. 2024

Applied Sciences

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Network protocol syntax information plays a crucial role in grammar-based fuzzing. Current network protocol syntax extraction methods are less versatile, inefficient, and the extracted information is not comprehensive. This paper proposes a novel method for extracting syntax information, which innovatively extracts network protocol syntax from Wireshark protocol dissector files. The extracted syntax information includes packet types of the protocol, the constituent fields of each packet type, and detailed attributes of each field. Based on this method, an automated system for network protocol syntax information extraction was developed. The experiment was conducted with this system on a variety of protocols including DCCP, DNP3.0, Modbus TCP, and S7COMM. The experimental results show that compared with the current methods, our method has a better performance in terms of efficiency and versatility and at the same time ensures the comprehensiveness and accuracy of the extracted syntax information.

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