Automated vulnerability detection of blockchain smart contacts based on BERT artificial intelligent model

Yiting Feng, Zhaofeng Ma, Pengfei Duan, Shoushan Luo
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Abstract

The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields. Cryptographic algorithms and smart contracts are critical components of blockchain security. Despite the benefits of virtual currency, vulnerabilities in smart contracts have resulted in substantial losses to users. While researchers have identified these vulnerabilities and developed tools for detecting them, the accuracy of these tools is still far from satisfactory, with high false positive and false negative rates. In this paper, we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model, which can quickly and effectively process and detect smart contracts. More specifically, we preprocess and make symbol substitution in the contract, which can make the pre-training model better obtain contract features. We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools, demonstrating its superior accuracy.
基于 BERT 人工智能模型的区块链智能触点漏洞自动检测
区块链技术的广泛应用促使人们探索其在各个领域的众多应用。加密算法和智能合约是区块链安全的关键组成部分。尽管虚拟货币好处多多,但智能合约中的漏洞给用户造成了巨大损失。虽然研究人员已经发现了这些漏洞并开发了检测工具,但这些工具的准确性仍远不能令人满意,误报率和误报率都很高。在本文中,我们提出了一种利用 BERT 预训练模型检测智能合约漏洞的新方法,它可以快速有效地处理和检测智能合约。具体来说,我们对合约进行了预处理和符号替换,这可以使预训练模型更好地获取合约特征。我们在四个数据集上对我们的方法进行了评估,并将其性能与其他深度学习模型和漏洞检测工具进行了比较,证明了其卓越的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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