Machine learning for preventing cyber-attacks on entrepreneurial crowdfunding platforms
1 Montclair State University, Montclair, New Jersey, USA.
2 CISCO, Nigeria.
3 Globacom Nigeria Limited.
4 Department of Computer Science, Texas Southern University, Texas, USA.
Review
Open Access Research Journal of Science and Technology, 2024, 12(02), 146-154.
Article DOI: 10.53022/oarjst.2024.12.2.0148
Publication history:
Received on 09 November 2024; revised on 22 December 2024; accepted on 24 December 2024
Abstract:
Entrepreneurial crowdfunding platforms have become a vital component of modern finance, connecting entrepreneurs with potential investors and enabling the flow of significant financial transactions. However, these platforms are increasingly vulnerable to cyber threats, including fraud, identity theft, and data breaches. Machine learning (ML) offers a dynamic solution to these challenges, providing real-time detection, prevention, and mitigation of cyberattacks. This paper reviews the role of machine learning in enhancing the security of crowdfunding platforms, focusing on specific ML algorithms suited for fraud detection, identity verification, and transaction monitoring. It also explores the integration of ML-based security tools into existing platform architectures, real-time detection mechanisms, and the challenges of implementing ML in cybersecurity, such as ethical concerns and limitations in addressing sophisticated attacks. The paper concludes by discussing future trends, including advanced AI models, collaborative defense systems, and cross-platform threat intelligence sharing, as crucial elements for improving the cybersecurity of entrepreneurial crowdfunding platforms.
Keywords:
Machine Learning; Cybersecurity; Crowdfunding Platforms; Fraud Detection; Identity Verification
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0