A Smart Contract Vulnerability Detection Method Based on Multimodal Feature Fusion and Deep Learning
Problem
Smart contracts on blockchains suffer security vulnerabilities (reentrancy, timestamp dependency, tx.origin, integer overflow) risking substantial user losses, while existing detection methods have low accuracy and typically target only one vulnerability type.
Why it matters
Vulnerable smart contracts can cause substantial financial losses to user accounts.
What the AI did
The paper proposes a deep learning method that fuses multimodal features extracted from smart contracts to detect multiple vulnerability types.
Results
not stated in source
Impact
Proposes a method to address limitations of prior detection approaches; impact not quantified in the abstract.
Record
| Venue | Mathematics |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Jinggang Li, Gehao Lu, Yulian Gao, Feng Gao |
| Citations | 29 (OpenAlex, at import time) |
| References | 39 |
| Model or method | Deep learning with multimodal feature fusion (details truncated in abstract) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/math11234823 |
| Open access | gold |
| Keywords | smart contracts, vulnerability detection, deep learning, multimodal feature fusion, blockchain |
Importance rating
real but narrower advance
Abstract states smart contract vulnerabilities pose substantial risk of losses to user accounts amid blockchain proliferation in DeFi, supply chain, identity management.
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