TL;DR
SmartGuard is presented, an automated multi-stage vulnerability analysis and detection system that integrates transformer-based code models with ensemble machine learning for comprehensive security assessment of Ethereum smart contracts.
The results demonstrate that the proposed framework provides accurate, interpretable, and transparent vulnerability detection for Ethereum smart contracts and can assist blockchain developers and security auditors in improving blockchain security analysis.
Findings demonstrate that the proposed framework provides a transparent and effective approach for smart contract vulnerability detection, supporting the development of trustworthy blockchain security analysis systems.
The findings indicate that hybrid feature engineering combined with explainable ensemble learning provides an effective and lightweight solution for Ethereum smart contract vulnerability detection and blockchain security analysis.
Smart contract vulnerabilities continue to threaten the security and reliability of blockchain-based applications, especially as blockchain systems are increasingly integrated into digital finance, supply chains, identity management, and other intelligent service environments. In these convergent digital systems, depen...
A novel neural network model is proposed that accommodates variable-length input sequences and advances the state-of-the-art by enabling truncation-free processing of long sequences, demonstrating superior performance over baselines reliant on fixed inputs.
Findings indicate that ensemble-based classifiers (Random Forest, CatBoost) are better suited to this structured smart contract feature representation than the evaluated deep learning baselines.
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