AI-Driven Unsupervised Framework for Detecting and Analyzing Anomalous Cryptocurrency Transactions Across Blockchain Networks Using Multi-Feature Analysis
An AI-driven framework for detecting anomalous transactions in cryptocurrency datasets using unsupervised learning techniques, specifically the Isolation Forest algorithm, demonstrates that unsupervised AI methods provide a robust, adaptive, and scalable solution for proactive fraud detection in blockchain networks.