Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols

Jero, Samuel; Pacheco, María Leonor; Goldwasser, Dan; Nita-Rotaru, Cristina

doi:10.1609/aaai.v33i01.33019478

Cited by 17 publications

(14 citation statements)

References 13 publications

(5 reference statements)

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“…AutoFuzz [3] AspFuzz [4] SecFuzz [5] Sulley [6] Boofuzz [7] Peach [8] Snooze [9] Pulsar [10] TLS-fuzzer [11] DTLS-fuzzer [12] ICS-fuzzer [13] NLP-fuzzer [14] SGPFuzzer…”

Section: Table I Survey Of Existing Protocol Fuzzersmentioning

confidence: 99%

“…As shown in Figure 1, the solid arrows represent normal state transitions caused by valid messages, while the dotted arrows denote transitioning to the initial state due to invalid received messages. [14] targeted at network protocols have been proposed and achieved some progress, several challenges remain. First, communication protocols are usually stateful, thus, the current message and the current internal server state are determined by earlier messages.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SGPFuzzer: A State-Driven Smart Graybox Protocol Fuzzer for Network Protocol Implementations

Yu¹,

Chen

Gan³

et al. 2020

IEEE Access

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As one of the most widely used technologies in software testing, fuzzing technology has been applied to network protocol vulnerability detection, and various network protocol fuzzers have been proposed. In this study, we first analyze and summarize some typical network protocol fuzzers to highlight the challenges when addressing stateful network protocol fuzzing. Then, a state-driven smart graybox protocol fuzzer (SGPFuzzer) is proposed to deal with these challenges. Finally, we evaluate SGPFuzzer on two widely used protocol implementations (LightFTP and tinyDTLS).The results show that SGPFuzzer outperforms Boofuzz and AFL in path coverage, unique crashes and the first time crash to crash, and it triggers a known bug which can't be trigged by the other two tools, fully proving its effectiveness and practicability. INDEX TERMS stateful network protocol, graybox fuzzer, AFL, smart mutation, Boofuzz S0 S1 S2 Sn ...

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“…AutoFuzz [3] AspFuzz [4] SecFuzz [5] Sulley [6] Boofuzz [7] Peach [8] Snooze [9] Pulsar [10] TLS-fuzzer [11] DTLS-fuzzer [12] ICS-fuzzer [13] NLP-fuzzer [14] SGPFuzzer…”

Section: Table I Survey Of Existing Protocol Fuzzersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SGPFuzzer: A State-Driven Smart Graybox Protocol Fuzzer for Network Protocol Implementations

Yu¹,

Chen

Gan³

et al. 2020

IEEE Access

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show abstract

“…We aim to bridge the gap between mentions in real word text and entities in well established theoretical schemas. A similar study that links mentions from RFC documents to a list of ontologies is conducted by Jero [19] to generate grammar-based fuzzing. Given limited training data, they generalized this problem by assigning the property with the maximum key phrase overlap to a header field.…”

Section: Entity Linkingmentioning

confidence: 99%

PEL-BERT: A Joint Model for Protocol Entity Linking

Li,

Cui,

Liu

et al. 2020

Preprint

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Pre-trained models such as BERT are widely used in NLP tasks and are fine-tuned to improve the performance of various NLP tasks consistently. Nevertheless, the fine-tuned BERT model trained on our protocol corpus still has a weak performance on the Entity Linking (EL) task. In this paper, we propose a model that joints a fine-tuned language model with an RFC Domain Model. Firstly, we design a Protocol Knowledge Base as the guideline for protocol EL. Secondly, we propose a novel model, PEL-BERT, to link named entities in protocols to categories in Protocol Knowledge Base. Finally, we conduct a comprehensive study on the performance of pre-trained language models on descriptive texts and abstract concepts. Experimental results demonstrate that our model achieves state-of-the-art performance in EL on our annotated dataset, outperforming all the baselines.

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“…Automatically or semi-automatically transforming intents and constraints into grammars that capture the space of admissible policies, would facilitate the interaction of end users with the policy-based management system being developed in AGENP. Previous work in software engineering [18], programming languages [19], and security [20] has investigated similar techniques but empirical analyses and further investigation of alternative approaches is required.…”

Section: B Research Directionsmentioning

confidence: 99%

Generative Policies for Coalition Systems - A Symbolic Learning Framework

Bertino

White²,

Lobo

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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Policy systems are critical for managing missions and collaborative activities carried out by coalitions involving different organizations. Conventional policy-based management approaches are not suitable for next-generation coalitions that will involve not only humans, but also autonomous computing devices and systems. It is critical that those parties be able to generate and customize policies based on contexts and activities. This paper introduces a novel approach for the autonomic generation of policies by autonomous parties. The framework combines context free grammars, answer set programs, and inductionbased learning. It allows a party to generate its own policies, based on a grammar and some semantic constraints, by learning from examples. The paper also outlines initial experiments in the use of such a symbolic approach and outlines relevant research challenges, ranging from explainability to quality assessment of policies.

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Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols

Cited by 17 publications

References 13 publications

SGPFuzzer: A State-Driven Smart Graybox Protocol Fuzzer for Network Protocol Implementations

SGPFuzzer: A State-Driven Smart Graybox Protocol Fuzzer for Network Protocol Implementations

PEL-BERT: A Joint Model for Protocol Entity Linking

Generative Policies for Coalition Systems - A Symbolic Learning Framework

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