AI-Based Smart Contract Analysis for Digital Transactions

  • Authors

    • Chen Ming Software Architect, Huawei, China. Author
    • Wang Li Data Science Manager, Alibaba Group, China. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2022PI4C8A

    Published 01-05-2022

  • Smart Contracts, Artificial Intelligence, Blockchain Security, Digital Transactions, Machine Learning, Vulnerability Detection, Static Analysis, Dynamic Analysis, Ethereum, Deep Learning

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    C. Ming and W. Li, “AI-Based Smart Contract Analysis for Digital Transactions”, IJAIDT, vol. 5, no. 1, pp. 01–16, Jan. 2022, doi: 10.67228/30713315/IJAIDT-2022PI4C8A.

  • Abstract

    Blockchain technology has transformed digital transactions into decentralized, transparent, and immutable systems. Smart contracts running on platforms like Ethereum enable automated agreements without intermediaries, but they remain vulnerable to logical errors and security risks that can lead to financial losses. This paper presents a systematic study on AI-based smart contract analysis, using machine learning and deep learning to detect vulnerabilities, anomalies, and potential risks. It proposes a hybrid model combining static analysis (code-level error detection), dynamic analysis (runtime monitoring), and supervised/unsupervised learning techniques. Feature extraction methods convert contract code into formats suitable for AI processing. The approach integrates rule-based systems with AI models to improve detection accuracy and reduce false positives. Evaluation on benchmark datasets shows better performance than traditional methods. The study highlights the effectiveness of AI in enhancing smart contract security and suggests future work on explainable AI, real-time monitoring, and cross-platform interoperability.

  • References

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