首页|Study Results from Beijing Jiaotong University in the Area of Ma- chine Learning Reported (A New Smart Contract Anomaly De- tection Method By Fusing Opcode and Source Code Features for Blockchain Services)
Study Results from Beijing Jiaotong University in the Area of Ma- chine Learning Reported (A New Smart Contract Anomaly De- tection Method By Fusing Opcode and Source Code Features for Blockchain Services)
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2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Digital assets involved in smart contracts are on the rise. Security vulnerabilities in smart contracts have resulted in significant losses for the blockchain community.” Financial support for this research came from Fundamental Research Funds for the Central Universities. Our news journalists obtained a quote from the research from Beijing Jiaotong University, “Existing smart contract vulnerability detection techniques have been typically single-purposed and focused only on the source code or opcode of contracts. This paper presents a new smart contract vulnerability detection method, which extracts features from different levels of smart contracts to train machine learning models for effective detection of vulnerabilities. Specifically, we propose to extract 2-gram features from the opcodes of smart contracts and token features from the source code using a pre-trained CodeBERT model, thereby capturing the semantic information of smart contracts at different levels. The 2-gram and token features are separately aggregated and then fused and input into machine-learning models to mine the vulnerability features of contracts. Over 10,266 smart contracts are used to verify the proposed method. Widespread reentrancy, timestamp dependence, and transaction-ordering dependence vulnerabilities are considered. Experiments show the fused features can help significantly improve smart contract vulnerability detection compared to the single-level features. The detection accuracy is as high as 98%, 98% and 94% for the three vulnerabilities, respectively.”
BeijingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningBeijing Jiaotong University
NETL
NSTL
IEEE
