A Review on Applications of Intelligent Intrusion Detection Using Machine Learning and IoT

The advent of Web 3.0 has brought about a paradigm shift in how decentralized applications (dApps) and smart contracts operate, leveraging blockchain technology to ensure transparency, immutability, and decentralization. However, the increasing complexity and adoption of these technologies have also introduced new vulnerabilities and attack vectors. Traditional intrusion detection systems (IDS) are often inadequate for addressing the unique challenges posed by Web 3.0 environments. This paper proposes a machine learning (ML)-based IDS tailored for dApps and smart contracts in Web 3.0. The proposed system leverages supervised and unsupervised learning techniques to detect anomalies, malicious activities, and potential exploits in real-time. We discuss the architecture, implementation challenges, and performance evaluation of the proposed IDS, highlighting its effectiveness in securing Web 3.0 ecosystems.