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.
Smart contracts play an important role in Ethereum blockchain systems by enabling decentralized and automated transactions without intermediaries. However, vulnerabilities in Solidity smart contracts remain a major security issue because attackers can exploit coding weaknesses such as reentrancy attacks, unsafe externa...
Smart contracts have become a fundamental component of blockchain ecosystems, enabling decentralized applications and automated transactions without intermediaries. However, vulnerabilities in smart contracts can lead to severe financial losses and security breaches, highlighting the need for effective automated vulner...
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.
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