首页|NDLSC: A New Deep Learning-based Approach to Smart Contract Vulnerability Detection
NDLSC: A New Deep Learning-based Approach to Smart Contract Vulnerability Detection
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NETL
NSTL
Springer Nature
With the rapid development of blockchain technology, the role of smart contracts as an important part of the blockchain has become more and more significant, bringing unprecedented value and innovation to many fields. However, despite the immense value created by smart contracts, their potential vulnerabilities have led to numerous attacks, resulting in substantial financial losses. Conventional expert-based detection methods, along with machine learning and deep learning techniques, frequently face challenges such as low accuracy and insufficient reliability. In order to address these issues, this paper puts forward a novel deep learning vulnerability detection method based on opcode-level analysis, designated as NDLSC. The method initially transforms smart contracts into opcodes, subsequently employing the Skip-Gram model in Word2Vec to vectorise the dataset. Subsequently, the Residual Networks 34(ResNet-34) deep learning model is utilized for feature extraction, followed by the Kolmogorov-Arnold Networks(KAN) model for further feature extraction and classification. This approach is employed with the objective of achieving superior results. The core algorithm of NDLSC, which combines ResNet and KAN, is experimentally compared with existing vulnerability detection techniques. The findings demonstrate that this combination not only enhances the precision of smart contract vulnerability identification but also fortifies the resilience of the model. By organically combining these two structures, the understanding and detection of smart contracts are significantly improved, making the detection process more precise and reliable.
Deep learningMachine learningSmart contractsVulnerability detectionBlockchain
School of Computer and Information Science, Hubei Engineering University, Xiaogan 432000, China||School of Computer Science, Hubei University, Wuhan, Hubei 430062, China
School of Computer and Information Science, Hubei Engineering University, Xiaogan 432000, China
NETL
NSTL
Springer Nature
