A Smart Contract Vulnerability Detection Mechanism Based on Deep Learning and Expert Rules

Liu, Zhenpeng; Jiang, Min; Zhang, Shengcong; Zhang, Jialiang; Liu, Yi

doi:10.1109/access.2023.3298048

Cited by 12 publications

(3 citation statements)

References 26 publications

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“…(12) conducted an empirical study on ChatGPT's performance in identifying smart contract vulnerabilities, revealing high recall rates but limited precision. (13) proposed a mechanism integrating graph neural networks and expert patterns to enhance smart contract vulnerability detection, showcasing superiority over existing methods. Meanwhile, (14) introduced ASSBert, a framework combining active and semi-supervised learning for efficient vulnerability detection with limited labelled data.…”

Section: Advancements In Smart Contract Vulnerability Detectionmentioning

confidence: 99%

Securing Smart Contracts: Harnessing the Power of Efficient NetB2 Detection

Satam,

Vhatkar

2024

IJST

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Objective: Using a variety of datasets from the Ethereum documentation and Smart Contract Dataset repository, this study tackles the crucial problem of classifying smart contract vulnerabilities. Methods: Our study uses a three-module method and focuses on the Resource 3 Dataset, which contains over 2,000 Ethereum smart contracts, including inherited contracts. The groundwork for deep learning model training is laid in Module 1 by extracting bytecode from Solidity files and creating images thereafter. In Colab, Module 2 entails importing data, pre-processing, SMOTE balancing, and building three deep learning models: CNN, XCEPTION, and EfficientNet-B2. Module 3 is a Flask-based web application created in Visual Studio Code that enables vulnerability predictions, bytecode extraction, and user interaction. Findings: With an overall accuracy of 71 percent, the Convolutional Neural Network (CNN) displays its effectiveness in classifying vulnerabilities. Although the accuracy of XCEPTION and EfficientNet-B2 is 69% and 75%, respectively, the latter is the top performer. Novelty & Applications: The online application adds to the comprehensive examination of smart contract security by giving users an easy-to-use interface. The EfficientNet-B2 model stands out as a dependable tool for precise vulnerability classification, and this study advances our understanding of and efforts to mitigate vulnerabilities in Ethereum smart contracts. Keywords: Smart Contracts, Vulnerability Classification, Ethereum, Deep Learning, Convolutional Neural Network (CNN)

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Section: Advancements In Smart Contract Vulnerability Detectionmentioning

confidence: 99%

Securing Smart Contracts: Harnessing the Power of Efficient NetB2 Detection

Satam,

Vhatkar

2024

IJST

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“…Their survey details the ongoing security challenges faced by smart contracts, evaluates existing solutions, and assesses their effectiveness, offering valuable insights into future research directions in smart contract security. Complementing this, Liu et al [18] introduced a smart contract vulnerability detection mechanism that combines deep learning with expert rules. This approach utilises graph neural networks alongside expert patterns, enhancing traditional detection methods that typically rely on fixed criteria.…”

Section: A Smart Contract Analysismentioning

confidence: 99%

MindTheDApp: A Toolchain for Complex Network-Driven Structural Analysis of Ethereum-Based Decentralized Applications

Ibba,

Aufiero,

Bartolucci

et al. 2024

IEEE Access

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This paper presents MindTheDApp, a toolchain designed specifically for the structural analysis of Ethereum-based Decentralized Applications (DApps), with a distinct focus on a complex network-driven approach. Unlike existing tools, our toolchain combines the power of ANTLR4 and Abstract Syntax Tree (AST) traversal techniques to transform the architecture and interactions within smart contracts into a specialized bipartite graph. This enables advanced network analytics to highlight operational efficiencies within the DApp's architecture. The bipartite graph generated by the proposed tool comprises two sets of nodes: one representing smart contracts, interfaces, and libraries, and the other including functions, events, and modifiers. Edges in the graph connect functions to smart contracts they interact with, offering a granular view of interdependencies and execution flow within the DApp. This network-centric approach allows researchers and practitioners to apply complex network theory in understanding the robustness, adaptability, and intricacies of decentralized systems. Our work contributes to the enhancement of security in smart contracts by allowing the visualisation of the network, and it provides a deep understanding of the architecture and operational logic within DApps. Given the growing importance of smart contracts in the blockchain ecosystem and the emerging application of complex network theory in technology, our toolchain offers a timely contribution to both academic research and practical applications in the field of blockchain technology.

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“…The Multi-Relational Nested Contract Graph (MRNG) scheme [62] characterizes the rich syntactic and semantic information in the SC code, including the relationships between data and instructions while Combining the graph feature and the expert patterns (CGE) method [63] is a fully automated approach for SC vulnerability detection at the function level. The paradigm followed by Liu et al [72] blocks risky transactions during the operation phase. In this method, the code violates the rules, the interrupt module is triggered, the code transaction is terminated, and an error report is issued.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Ethereum Smart Contract Vulnerability Detection and Machine Learning-Driven Solutions: A Systematic Literature Review

Kiani,

Sheng

2024

Electronics

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In recent years, emerging trends like smart contracts (SCs) and blockchain have promised to bolster data security. However, SCs deployed on Ethereum are vulnerable to malicious attacks. Adopting machine learning methods is proving to be a satisfactory alternative to conventional vulnerability detection techniques. Nevertheless, most current machine learning techniques depend on sufficient expert knowledge and solely focus on addressing well-known vulnerabilities. This paper puts forward a systematic literature review (SLR) of existing machine learning-based frameworks to address the problem of vulnerability detection. This SLR follows the PRISMA statement, involving a detailed review of 55 papers. In this context, we classify recently published algorithms under three different machine learning perspectives. We explore state-of-the-art machine learning-driven solutions that deal with the class imbalance issue and unknown vulnerabilities. We believe that algorithmic-level approaches have the potential to provide a clear edge over data-level methods in addressing the class imbalance issue. By emphasizing the importance of the positive class and correcting the bias towards the negative class, these approaches offer a unique advantage. This unique feature can improve the efficiency of machine learning-based solutions in identifying various vulnerabilities in SCs. We argue that the detection of unknown vulnerabilities suffers from the absence of a unique definition. Moreover, current frameworks for detecting unknown vulnerabilities are structured to tackle vulnerabilities that exist objectively.

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A Smart Contract Vulnerability Detection Mechanism Based on Deep Learning and Expert Rules

Cited by 12 publications

References 26 publications

Securing Smart Contracts: Harnessing the Power of Efficient NetB2 Detection

Securing Smart Contracts: Harnessing the Power of Efficient NetB2 Detection

MindTheDApp: A Toolchain for Complex Network-Driven Structural Analysis of Ethereum-Based Decentralized Applications

Ethereum Smart Contract Vulnerability Detection and Machine Learning-Driven Solutions: A Systematic Literature Review

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