Securing blockchain-based crowdfunding platforms: an integrated graph neural networks and machine learning approach

Blockchain-based crowdfunding is a form of crowdfunding that uses blockchain technology to facilitate the fundraising process. Blockchain technology provides a decentralized, transparent, and secure platform for crowdfunding by allowing the creation of smart contracts and the issuance of digital tokens. However, Blockchain-based crowdfunding systems suffer from a few security issues, such as the possibility of fraud, risk assessment, smart contracts bugs, and cyber-attacks. This paper proposes integrating artificial intelligence models to prevent smart contract vulnerabilities and anomaly detection. Thus, we will deploy Graph Neural Networks models to protect Blockchain-based crowdfunding platforms from smart contracts-based attacks such as reentrancy and infinite loop attacks. Then, we will use a machine learning model for anomaly detection and prevent attacks such as advanced persistent threats, malware, and distributed denial of service attacks. An experimental study is conducted in a real crowdfunding platform to prove the feasibility of our framework and to draw lessons from the real-life implementation of such models. Our results show that our approach can accurately identify both normal and abnormal traffic and classify correctly specific types of attacks. We also evaluate the performance of our framework using various evaluation metrics to ensure its effectiveness in detecting anomalies.