HyMo：使用新型多模态混合模型进行智能合约漏洞检测
HyMo: Vulnerability Detection in Smart Contracts using a Novel Multi-Modal Hybrid Model
AI总结：
针对智能合约漏洞检测依赖专家规则且复杂合约检测耗时的问题，提出融合多模态输入、FastText 嵌入与 BiGRU 的混合深度学习模型 HyMo，并在 ScrawlD 上验证其检测性能优于现有方法。
摘要：
随着区块链技术的快速发展，智能合约已成为金融、医疗、保险和游戏等多个行业中的常用工具。智能合约的数量成倍增长，与此同时，由于智能合约漏洞所造成的经济损失，智能合约的安全性引起了广泛关注。现有的分析技术能够识别大量智能合约安全缺陷，但它们过度依赖专家制定的僵化标准，并且随着智能合约复杂性的增加，检测过程需要更长时间。本文提出 HyMo，这是一种多模态混合深度学习模型，它智能地考虑多种输入表示以体现多模态性，并采用 FastText 词嵌入技术（该技术将每个词表示为字符的 n-gram）与 BiGRU 深度学习技术相结合；BiGRU 是一种由两个 GRU 组成的序列处理模型，旨在实现更高的智能合约漏洞检测准确率。该模型利用多种深度学习模型收集特征，以识别智能合约漏洞。通过在 ScrawlD 等当前公开可用的数据集上开展一系列研究，我们证明混合 HyMo 模型具有出色的智能合约漏洞检测性能。因此，与其他方法相比，HyMo 能够更好地检测智能合约漏洞。
英文摘要：
With blockchain technology rapidly progress, the smart contracts have become a common tool in a number of industries including finance, healthcare, insurance and gaming. The number of smart contracts has multiplied, and at the same time, the security of smart contracts has drawn considerable attention due to the monetary losses brought on by smart contract vulnerabilities. Existing analysis techniques are capable of identifying a large number of smart contract security flaws, but they rely too much on rigid criteria established by specialists, where the detection process takes much longer as the complexity of the smart contract rises. In this paper, we propose HyMo as a multi-modal hybrid deep learning model, which intelligently considers various input representations to consider multimodality and FastText word embedding technique, which represents each word as an n-gram of characters with BiGRU deep learning technique, as a sequence processing model that consists of two GRUs to achieve higher accuracy in smart contract vulnerability detection. The model gathers features using various deep learning models to identify the smart contract vulnerabilities. Through a series of studies on the currently publicly accessible dataset such as ScrawlD, we show that our hybrid HyMo model has excellent smart contract vulnerability detection performance. Therefore, HyMo performs better detection of smart contract vulnerabilities against other approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
