Enhancing Smart-Contract Security through Machine Learning: A Survey of Approaches and Techniques
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TL;DR: Wang et al. as discussed by the authors presented a comprehensive investigation of smart-contract vulnerability detection based on machine learning, elucidate common types of smart contract vulnerabilities and the background of formalized vulnerability detection tools.
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Abstract: 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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Citations
VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model
Phan The Duy,Nghi Hoang Khoa,Nguyen Huu Quyen,Le Cong Trinh,V. Kiên,Trinh Minh Hoang,Van-Hau Pham +6 more
TL;DR: VulnSense framework is presented, a comprehensive approach to efficiently detect vulnerabilities in Ethereum smart contracts using a multimodal learning approach on graph-based and natural language processing (NLP) models that surpasses accuracy and effectiveness constraints.
Vulnerability Detection in Smart Contracts: A Comprehensive Survey
Christopher De Baets,Basem Suleiman,Armin Chitizadeh,Imran Razzak +3 more
- 08 Jul 2024
TL;DR: This comprehensive survey integrates machine learning to enhance smart contract security, revealing classical techniques outperform static tools in vulnerability detection, and multi-model approaches achieving near-perfect performance in precision and recall.
Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain
Anand Singh Rajawat,S. B. Goyal,Mithilesh Kumar,Saurabh Kumar +3 more
TL;DR: Quantum deep neural network based classification of attack vectors on the Ethereum blockchain enhances security by leveraging quantum computing to identify and predict potential attacks.
1
Securing Smart Contracts in Fog Computing: Machine Learning-Based Attack Detection for Registration and Resource Access Granting
Tahmina Ehsan,Muhammad Usman Sana,Muhammad Usman Ali,Elizabeth Caro Montero,Eduardo Silva Alvarado,Sirojiddin Djuraev,Imran Ashraf +6 more
1
Contract-based hierarchical security aggregation scheme for enhancing privacy in federated learning
Qingfeng Wei,G. Mallikharjuna Rao,X. Wu +2 more
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