A novel extended multimodal AI framework towards vulnerability detection in smart contracts

Jie, Wanqing; Chen, Qi; Wang, Jiaqi; Koe, Arthur Sandor Voundi; Li, Jin; Huang, Pengfei; Wu, Yiyan; Wang, Yin

doi:10.1016/j.ins.2023.03.132

Cited by 18 publications

(4 citation statements)

References 29 publications

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“…Meanwhile, (14) introduced ASSBert, a framework combining active and semi-supervised learning for efficient vulnerability detection with limited labelled data. (15) presented a white box knowledge-enforcing methodology, outperforming existing schemes in multimodal vulnerability mining. (16) proposed a Graph Neural Network-based approach, achieving an impressive 89.2% precision and 92.9% recall in smart contract vulnerability detection.…”

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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“…Using a multi-layer bidirectional Transformer structure and using CodeBERT, VDDL was introduced by [60]. The multi-modal AI framework developed by [61] incorporated NLP, IR, and coding analysis methods. Using active SSL to combat the problem of insufficient labeled data and relying on bidirectional encoder representations from Transformers (BERT), Ref.…”

Section: Ai For Smart Contract Securitymentioning

confidence: 99%

Strengthening the Security of Smart Contracts through the Power of Artificial Intelligence

Krichen

2023

Computers

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Smart contracts (SCs) are digital agreements that execute themselves and are stored on a blockchain. Despite the fact that they offer numerous advantages, such as automation and transparency, they are susceptible to a variety of assaults due to their complexity and lack of standardization. In this paper, we investigate the use of artificial intelligence (AI) to improve SC security. We provide an overview of Smart Contracts (SCs) and blockchain technology, as well as a discussion of possible SC-based attacks. Then, we introduce various AI categories and their applications in cybersecurity, followed by a thorough analysis of how AI can be used to enhance SC security. We also highlight the open questions and future directions of research in this field. Our research demonstrates that AI can provide an effective defense against assaults on SCs and contribute to their security and dependability. This article lays the groundwork for future research in the field of AI for SC security.

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“…Furthermore, VDDL incorporated CodeBERT, a large-scale dual-modal pretraining model for natural language and programming language, to enhance training results. Jie et al [131] used a multi-modal artificial intelligence framework to detect vulnerabilities in smart contracts. The framework combined various techniques such as natural language processing, image processing, and code analysis and employed machine-learning algorithms like support vector machines (SVM) and long short-term memory networks (LSTM) to improve vulnerability detection accuracy and efficiency.…”

Section: Machine-learning-based Tools For Smart-contract Vulnerabilit...mentioning

confidence: 99%

Enhancing Smart-Contract Security through Machine Learning: A Survey of Approaches and Techniques

et al. 2023

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As blockchain technology continues to advance, smart contracts, a core component, have increasingly garnered widespread attention. Nevertheless, security concerns associated with smart contracts have become more prominent. Although machine-learning techniques have demonstrated potential in the field of smart-contract security detection, there is still a lack of comprehensive review studies. To address this research gap, this paper innovatively presents a comprehensive investigation of smart-contract vulnerability detection based on machine learning. First, we elucidate common types of smart-contract vulnerabilities and the background of formalized vulnerability detection tools. Subsequently, we conduct an in-depth study and analysis of machine-learning techniques. Next, we collect, screen, and comparatively analyze existing machine-learning-based smart-contract vulnerability detection tools. Finally, we summarize the findings and offer feasible insights into this domain.

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A novel extended multimodal AI framework towards vulnerability detection in smart contracts

Cited by 18 publications

References 29 publications

Securing Smart Contracts: Harnessing the Power of Efficient NetB2 Detection

Securing Smart Contracts: Harnessing the Power of Efficient NetB2 Detection

Strengthening the Security of Smart Contracts through the Power of Artificial Intelligence

Enhancing Smart-Contract Security through Machine Learning: A Survey of Approaches and Techniques

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