Combatting Cybersecurity Threats on Social Media: Network Protection and Data Integrity Strategies

Authors

  • Ashraf jalal yousef Zaidieh Faculty applied college, Imam Mohammad Ibn Saud Islamic University, Saudi Arabia

DOI:

https://doi.org/10.70274/jaict.2024.1.1.32

Abstract

The rise of social media has transformed global communication, but it has also introduced significant cybersecurity threats, including identity theft, phishing, malware distribution, and data breaches. These challenges not only compromise individual users but also pose risks to businesses and governments. This research explores the prevalent cybersecurity threats on social media and proposes an integrated framework to enhance network protection and data integrity. The framework combines both technical solutions such as encryption, multi-factor authentication, and AI-based threat detection and non-technical strategies like user education, platform policies, and collaborative efforts among stakeholders. By synthesizing findings from a comprehensive literature review, this study identifies the most common cyber threats and assesses their impacts on users, businesses, and society at large. The research highlights the importance of proactive measures, including real-time monitoring, secure data practices, and user behavior modification, to mitigate these risks. Additionally, the study emphasizes the need for greater collaboration between platform providers, governments, and users to create a safer digital environment. The proposed framework is flexible and applicable across various social media platforms, providing a holistic approach to combatting evolving cyber threats. This study contributes to the growing body of knowledge on social media cybersecurity, offering practical recommendations for improving security and maintaining the integrity of online networks.

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Published

2024-11-08

How to Cite

yousef Zaidieh, A. jalal . (2024). Combatting Cybersecurity Threats on Social Media: Network Protection and Data Integrity Strategies. Journal of Artificial Intelligence and Computational Technology, 1(1). https://doi.org/10.70274/jaict.2024.1.1.32

Issue

Section

Articles