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.

翻译：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的常见工具。智能合约的数量成倍增长，同时，由于智能合约漏洞导致的经济损失，其安全性引起了广泛关注。现有分析技术能够识别大量智能合约安全缺陷，但过度依赖专家设定的刚性标准，导致检测过程随智能合约复杂度的提升而显著延长。本文提出HyMo多模态混合深度学习模型，该模型智能地考虑多种输入表示以融合多模态特性，并引入FastText词嵌入技术（将每个词表示为字符级n-gram）与BiGRU深度学习技术（由两个GRU组成的序列处理模型），从而在智能合约漏洞检测中实现更高精度。该模型通过多种深度学习模型提取特征以识别智能合约漏洞。基于当前公开数据集（如ScrawlD）的一系列实验表明，我们的混合模型HyMo具有优异的智能合约漏洞检测性能。因此，HyMo在智能合约漏洞检测方面优于其他方法。