Smart Contracts Vulnerability Analysis Pdf This paper examines novel ai driven techniques for vulnerability detection in smart contracts, focusing on machine learning, deep learning, graph neural networks, and transformer based models. Traditional auditing methods often struggle to capture the more intricate vulnerabilities hidden within smart contract logic, particularly owing to the irreversible nature of blockchain.
Ai Driven Vulnerability Analysis In Smart Contracts Trends Challenges This fragmented landscape leaves a noticeable gap for practitioners looking for a well rounded understanding of smart contract security solutions. to address this, our study set out to systematically review the existing body of work, analysing 21 reviewed studies published between 2020 and 2024. Ai driven vulnerability analysis in smart contracts: trends, challenges and future directions this paper discusses the application of ai driven techniques for vulnerability detection in smart contracts, highlighting the limitations of traditional auditing methods. Traditional auditing methods often struggle to capture the more intricate vulnerabilities hidden within smart contract logic, particularly owing to the irreversible nature of blockchain transactions. given these challenges, researchers have been actively exploring more advanced detection techniques. Addressing smart contract vulnerability mining in multimodal ai settings presents four major technical challenges. first, comprehend the nature of the raw features to be extracted, as well as their significance in terms of vulnerability detection performance.
Ai Driven Vulnerability Analysis In Smart Contracts Trends Challenges Traditional auditing methods often struggle to capture the more intricate vulnerabilities hidden within smart contract logic, particularly owing to the irreversible nature of blockchain transactions. given these challenges, researchers have been actively exploring more advanced detection techniques. Addressing smart contract vulnerability mining in multimodal ai settings presents four major technical challenges. first, comprehend the nature of the raw features to be extracted, as well as their significance in terms of vulnerability detection performance. In the short history of smart contracts, substantial losses have occurred due to unaccounted vulnerabilities in the smart contracts loaded onto the blockchain. Ai driven smart contract vulnerability analysis with machine learning, deep learning, graphs, and transformers; trends, challenges, future directions. This section provides an overview of the core ai ml concepts used in the reviewed studies and establishes the foundation for the subsequent analysis of ai driven vulnerability detection and mitigation. Ai based smart contract security analysis represents a significant advance over traditional methods. while not perfect, these techniques offer scalable, automated ways to find vulnerabilities before they can be exploited.
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