Machine learning-driven cybersecurity for social media data protection in entrepreneurial ventures
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
International Journal of Scientific Research Updates, 2024, 08(02), 175-184.
Article DOI: 10.53430/ijsru.2024.8.2.0070
Publication history:
Received on 09 November 2024; revised on 22 December 2024; accepted on 24 December 2024
Abstract:
This review paper explores the application of machine learning-driven cybersecurity solutions to enhance social media data protection, with a specific focus on entrepreneurial ventures. Social media platforms are integral to business operations, especially for startups, but they pose significant cybersecurity risks, including phishing, malware, and data breaches. Machine learning offers innovative approaches to detecting and mitigating these threats through real-time anomaly detection, deep learning, and AI-driven threat intelligence. However, challenges such as evolving cyber threats, privacy concerns, and the high implementation costs present barriers for small businesses. This paper examines the current cybersecurity landscape, the role of machine learning in addressing these risks, and provides recommendations for startups to enhance social media data protection. Emerging trends, such as the use of deep learning and AI-driven threat intelligence, are also discussed, alongside best practices for entrepreneurial ventures in adopting scalable and effective ML solutions.
Keywords:
Machine Learning; Cybersecurity; Social Media; Entrepreneurial Ventures; Data Protection; Deep Learning
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Copyright information:
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