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Research Article Open access CC BY 4.0

The Synergistic Role of Machine Learning, Deep Learning, and Reinforcement Learning in Strengthening Cyber Security Measures for Crypto Currency Platforms

Abayomi Titilola Olutimehin

Asian Journal of Research in Computer Science · pp. 190–212 · Published 14 Feb 2025

10.9734/ajrcos/2025/v18i3586

Abstract

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 model demonstrates a 5.2% increase in fraud detection accuracy over traditional rule-based methods while reducing false positives by 19.3%. However, the model exhibits a false negative rate of 98.9%, indicating challenges in fully capturing sophisticated fraud techniques. Regression analysis shows a strong inverse correlation (R² = 0.927) between AI adoption and fraud cases, where each 1% increase in AI adoption corresponds to a reduction of approximately 37 fraud cases.In real-world applicability, the proposed AI-driven models enhance scalability and real-time threat detection but require substantial computational resources, particularly for deep learning and RL-based techniques. Computational efficiency is optimized through federated learning and quantum-resistant AI security, ensuring robust yet privacy-preserving fraud detection. Despite its advantages, challenges such as adversarial AI attacks, regulatory inconsistencies, and scalability under high transaction loads persist.The study recommends self-supervised learning for fraud detection, improving interpretability in deep learning models, and developing AI-driven compliance frameworks to address ethical concerns. By integrating Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), this study provides a novel approach to securing cryptocurrency transactions, offering actionable insights for researchers, financial institutions, and policymakers.

Cryptocurrency security machine learning fraud detection smart contracts AI-driven cybersecurity

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