TL;DR
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.
This paper proposes SVACS, a bytecode-based analysis framework to identify multiple co-existing vulnerabilities based on SWC registry to align with industry-standard security guidelines and assist security reviewers to ensure smart contract security.
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.
A unified framework combining a novel Hierarchical Cross-Attention Subgraph Neural Network for detection with Large Language Models for explanation form a comprehensive framework that significantly enhances both the technical accuracy and operational usability of smart contract analysis.
SE4SC-LLM, an LLM-augmented symbolic execution framework for smart contracts that achieves 95.1% average CFG coverage, a 6.5 percentage point improvement over the strongest baseline, and detects 11.2% more vulnerabilities.
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