Title: "A Multimodal Deep Learning Approach for Efficient Vulnerability Detection in Smart Contracts"
Paper Link: https://drive.google.com/file/d/1KKhsMLzhL1gBLzCDPiqYfn6kqw43xtR9/view
Students:
- Lê Công Trình – 19522404 – ATC2019.2 (Primary Author)
- Vũ Trung Kiên – 19521722 – ATC2019.2 (Co-Author)
- Trịnh Minh Hoàng – 19521548 – ATC2019.2 (Co-Author)
Supervisors:
- ThS. Phan Thế Duy
- TS. Phạm Văn Hậu
Abstract:
In this paper, we present a comprehensive approach for efficient vulnerability detection in Ethereum smart contracts using a multimodal deep learning (DL) approach. Our proposed approach combines two levels of features in smart contracts, including source code, bytecode, and utilizes BERT and Bi-LSTM models to extract and analyze the features. The last layer of our multimodal approach is a fully connected layer that predicts the vulnerability in Ethereum smart contracts. We address the limitations of existing deep learning-based vulnerability detection methods for smart contracts, which often rely on a single type of feature or model, resulting in limited accuracy and effectiveness. The experimental results show that our proposed approach achieves superior results compared to existing state-of-the-art methods, demonstrating the effectiveness and potential of multimodal DL approaches in smart contract vulnerability detection.
The students extend their gratitude to Thầy Phạm Văn Hậu, Head of the Information Security Department, Thầy Phan Thế Duy, Deputy Head of the Information Security Lab, and other faculty members for their dedicated guidance and feedback during the research and publication process of this international scientific paper.
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Written by: Hai Bang
Translated by: Ngoc Diem