论文标题
SmartIntentnn：朝着智能合同意图检测
SmartIntentNN: Towards Smart Contract Intent Detection
论文作者
论文摘要
区块链上的智能合约提供分散的金融服务，但通常缺乏强大的安全措施，从而导致巨大的经济损失。尽管大量研究重点是确定智能合约中的漏洞，但仍有一个显着的差距在评估其发展背后的恶意意图。为了解决这个问题，我们介绍了\ textsc {smartIntentnn}（智能合同意图神经网络），这是一种基于深度学习的工具，旨在自动化开发人员在智能合约中的意图。我们的方法集成了一个通用句子编码器，用于上下文表示智能合约代码的上下文表示，采用K均值聚类算法来突出与意图相关的代码功能，并利用基于双向LSTM的多标签分类网络来预测十个不同类别的不安全意图。对10,000个现实世界智能合约的评估表明，\ textsc {smartintentnn}超过所有基线，达到0.8633的F1得分。 可以在\ url {https://youtu.be/ott0fdyjwk8}上获得演示视频。
Smart contracts on the blockchain offer decentralized financial services but often lack robust security measures, leading to significant economic losses. While substantial research has focused on identifying vulnerabilities in smart contracts, a notable gap remains in evaluating the malicious intent behind their development. To address this, we introduce \textsc{SmartIntentNN} (Smart Contract Intent Neural Network), a deep learning-based tool designed to automate the detection of developers' intent in smart contracts. Our approach integrates a Universal Sentence Encoder for contextual representation of smart contract code, employs a K-means clustering algorithm to highlight intent-related code features, and utilizes a bidirectional LSTM-based multi-label classification network to predict ten distinct categories of unsafe intent. Evaluations on 10,000 real-world smart contracts demonstrate that \textsc{SmartIntentNN} surpasses all baselines, achieving an F1-score of 0.8633. A demo video is available at \url{https://youtu.be/otT0fDYjwK8}.
