Year: 2024
Type: article
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.
Institutions: Montclair State University, Texas Southern University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405870491