Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models

IEEE Access

卷:8

Qinming He¹  Xun Wang²  Peng Qian²  Zhenguang Liu²  Roger Zimmermann³ 

[1] Department of Computer Science, Zhejiang University, Hangzhou, China;

[2] School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, China;

[3] School of Computing, National University of Singapore, Singapore;

关键词: Blockchain;    smart contract;    deep learning;    sequential models;    vulnerability detection;   

DOI  :  10.1109/ACCESS.2020.2969429

来源: DOAJ

【 摘 要 】

In the last decade, smart contract security issues lead to tremendous losses, which has attracted increasing public attention both in industry and in academia. Researchers have embarked on efforts with logic rules, symbolic analysis, and formal analysis to achieve encouraging results in smart contract vulnerability detection tasks. However, the existing detection tools are far from satisfactory. In this paper, we attempt to utilize the deep learning-based approach, namely bidirectional long-short term memory with attention mechanism (BLSTM-ATT), aiming to precisely detect reentrancy bugs. Furthermore, we propose contract snippet representations for smart contracts, which contributes to capturing essential semantic information and control flow dependencies. Our extensive experimental studies on over 42,000 real-world smart contracts show that our proposed model and contract snippet representations significantly outperform state-of-the-art methods. In addition, this work proves that it is practical to apply deep learning-based technology on smart contract vulnerability detection, which is able to promote future research towards this area.

【 授权许可】

Unknown