A Hybrid Artificial Intelligence and Blockchain Framework for Enhancing Security and Privacy in Web ۳.۰ Ecosystems

The evolution of the internet to Web ۳.۰, driven by decentralized technologies like blockchain, has brought about a new paradigm of user-centricity and openness. However, the ecosystem is faced with significant security challenges, including smart contract vulnerabilities, illicit financial transactions, and emerging cyber-attacks, undermining user trust and hindering mass adoption. This study aims to conceptualize, deploy, and evaluate a new hybrid security model that enhances the security and resilience of the Web ۳.۰ platform. We have proposed a comprehensive approach that leverages the strengths of Artificial Intelligence (AI) and blockchain technology. We use a Convolutional Neural Network (CNN) as an intelligent engine for detecting real-time threats and anomalies. This AI component is synergistically combined with a blockchain layer as an immutable, tamper-evident ledger for security events logging and automated incident response through smart contracts. The performance of the framework was exhaustively evaluated on two very different, real-world datasets: Elliptic for identifying illegal transactions and DAppSCAN for the detection of smart contract vulnerabilities. In both instances, our model considerably outperformed a collection of state-of-the-art and traditional baseline models on all classical metrics, including accuracy, precision, recall, and F۱-score. Empirical evidence confirms that our hypothesis of a hybrid AI-blockchain solution provides a stronger and superior Web ۳.۰ security solution over single-paradigm solutions. The foreseen framework marks a significant milestone towards constructing a more secure, dependable, and robust decentralized internet for users, developers, and organizations.

کلیدواژه ها:

Web ۳.۰ ، Cybersecurity ، Blockchain ، Artificial Intelligence ، Deep Learning ، Threat Detection