SmartIntentNN: Towards Smart Contract Intent Detection
Abstract
A deep learning system detects developer intent in smart contracts using sentence encoding, clustering, and LSTM classification to identify potentially unsafe code patterns.
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 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 SmartIntentNN surpasses all baselines, achieving an F1-score of 0.8633. A demo video is available at https://youtu.be/otT0fDYjwK8.
Models citing this paper 0
No model linking this paper
Datasets citing this paper 1
Spaces citing this paper 0
No Space linking this paper
Collections including this paper 0
No Collection including this paper