Improving Smart Contract Security: Identifying Vulnerability Anti-Patterns in Solidity to Reduce False Alarms
By identifying and addressing common code patterns that trigger false positives in Solidity vulnerability detection tools, developers can significantly improve the efficiency of security audits and focus on genuine threats.
SMARTINTENTNN: A Deep Learning Approach to Detecting Malicious Intent in Smart Contracts
SMARTINTENTNN is a novel deep learning-based tool that effectively detects malicious intent hidden within smart contracts by analyzing code functionality and highlighting suspicious patterns.
Deep Learning for Detecting Malicious Intent in Smart Contracts
To protect users from financial losses due to malicious smart contracts, this paper introduces SMARTINTENTNN, a novel deep learning model that effectively detects unsafe developer intents hidden within smart contract code.
Analysis of Secure and Trusted Smart Contracts Design
Smart contracts must be secure and trusted-by-design to eliminate the need for third-party trust.
SmartML: Modeling Language for Smart Contracts
SmartML proposes a modeling language for smart contracts to enhance security and prevent reentrancy vulnerabilities.
ACFIX: Guiding LLMs with Mined Common RBAC Practices for Context-Aware Repair of Access Control Vulnerabilities in Smart Contracts
ACFIX enhances GPT-4 model to repair AC vulnerabilities in smart contracts by mining common RBAC practices and utilizing context information.
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