A Novel Extended Multimodal Ai Framework Towards Vulnerability Detection in Smart Contracts

Jie, Wanqing; Wang, Jiaqi; Koe, Arthur Sandor Voundi; Li, Jin; Chen, Qi; Huang, Pengfei; Wu, Yiyan; Wang, Yin

doi:10.2139/ssrn.4331099

Cited by 1 publication

(2 citation statements)

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“…Random forest and support vector machines have been applied to smart contract vulnerability detection [19][20][21]. Recent works like [22][23][24] have expanded the scope by focusing on multiple types of vulnerabilities, offering a more comprehensive vulnerability detection mechanism.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

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VdaBSC: A Novel Vulnerability Detection Approach for Blockchain Smart Contract by Dynamic Analysis

Sosu,

Chen,

Boahen

et al. 2023

IET Software

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Smart contracts have gained immense popularity in recent years as self-executing programs that operate on a blockchain. However, they are not immune to security flaws, which can result in significant financial losses. These flaws can be detected using dynamic analysis methods that extract various aspects from smart contract bytecode. Methods currently used for identifying vulnerabilities in smart contracts mostly rely on static analysis methods that search for predefined vulnerability patterns. However, these patterns often fail to capture complex vulnerabilities, leading to a high rate of false negatives. To overcome this limitation, researchers have explored machine learning-based methods. However, the accurate interpretation of complex logic and structural information in smart contract code remains a challenge. In this study, we present a technique that combines real-time runtime batch normalization and data augmentation for data preprocessing, along with n-grams and one-hot encoding for feature extraction of opcode sequence information from the bytecode. We then combined bidirectional long short-term memory (BiLSTM), convolutional neural network, and the attention mechanism for vulnerability detection and classification. Additionally, our model includes a gated recurrent units memory module that enhances efficiency using historical execution data from the contract. Our results demonstrate that our proposed model effectively identifies smart contract vulnerabilities.

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Section: Machine Learning Methodsmentioning

confidence: 99%

“…This study employs feature selection techniques that surpass conventional methods' [23,36] scope, adeptly assimilating local and global features to construct a more resilient model.…”

Section: Data Preprocessingmentioning

confidence: 99%