Original research demonstrates improved fraud detection in cryptocurrency platforms using AI models like machine learning and reinforcement learning, indicating effective cybersecurity solutions.
23 Pages Posted: 5 Mar 2025 The University of Tampa Date Written: February 11, 2025 This study explores the role of artificial intelligence (AI)-driven cybersecurity models in mitigating fraud, smart contract vulnerabilities, and regulatory challenges in cryptocurrency platforms. Utilizing datasets such as the Elliptic Bitcoin Dataset, SolidiFI-Benchmark, CryptoScamDB, and CipherTrace AML Reports, this research employs Logistic Regression, Random Forest, and Reinforcement Learning (RL) for fraud detection and anomaly identification. The AI-based security Original Research Article (DL), and Reinforcement Learning (RL), this study provides a novel approach to securing cryptocurrency transactions, offering actionable insights for researchers, financial institutions, and policymakers. Keywords: Cryptocurrency security, machine learning, fraud detection, smart contracts, AI-driven cybersecurity Suggested Citation: Suggested Citation Computing Technologies eJournal Subscribe to this fee journal for more curated articles on this topic Information Technology & Systems eJournal Subscribe to this fee journal for more curated articles on this topic
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Abayomi Titilola Olutimehin (2025) studied this question.
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