Enhanced Social Network Security System: Integrating Biometric Authentication for Improved User Verification and Privacy Protection

Social networks are becoming a necessary component of contemporary life, yet security is still a major worry. Conventional password-based authentication methods are susceptible to a number of threats that jeopardize user privacy and data. In order to offer strong user verification and privacy protection, this study suggests an enhanced social network security system that incorporates biometric authentication; through the use of fingerprint scanning. The Enhanced Social Network Security System establishes a biometric authentication system that restricts access to vital websites, networks, and web-based applications to those who are approved, as used in this study which protect user biometric data integrity; when compared to conventional password-based systems. The suggested solution exhibits better security and usability. According to experimental results, erroneous acceptance and rejection rates have significantly decreased, guaranteeing precise user verification. The System offers social networks a practical and safe authentication solution that safeguards user information and privacy. The use of biometric authentication improves social networks' overall security posture and increases their resistance to online attacks.

Biometric authentication, social network security, user verification, privacy protection, machine learning

1. Sharma, S. and Soldhi, J.S. (2014). Implementation of Biometric Techniques in Social Networking sites. International Journal of Security and its Applications. Volume 8.No. 6. Pp. 51-60. [Google Scholar] [Crossref]

2. Dabbour, M., Alsmadi, I., and Emad, A. “Efficient Assessment and Evaluation for Websites Vulnerabilities Using SNORT”. International Journal of Security and Its Applications Vol. 7, No.1, January, 2013. [Google Scholar] [Crossref]

3. Sponcil, M. and Gitimu, P. Use of social media by college students: Relationship to communication and self-concept. Journal of Technology Research [Google Scholar] [Crossref]

4. Collin, P., Rahilly, K., Richardson, I. & Third, A. (2011). The Benefits of Social Networking Services: A literature review. Cooperative Research Centre for Young People, Technology and Wellbeing. Melbourne. ISBN: 978-0-9871179-1-5. [Google Scholar] [Crossref]

5. Steinfield, C., Ellison, N., Lampe, C. and Vitak, J. (2012). Online Social Network Sites and the Concept of Social Capital. Frontiers in new Media Research, New York: Routledge, pp 115-131. [Google Scholar] [Crossref]

6. Matthew O. Jackson. (2007). The Study of Social Networks in Economics. Prepared for The Missing Links: Formation and Decay of Economic Networks. [Google Scholar] [Crossref]

7. Singh, V., Beniwal, Bulbul, D., and Tomer, J. (2015). Why Adolescents Use Social Networking Sites: A Gender-based Analysis. Afro Asian Journal of Social Sciences Volume VI, No 1. Quarter I. ISSN: 2229 – 5313 [Google Scholar] [Crossref]

8. Dewing, M. (2012). Social Media: An Introduction. Parliamentary and Research Service, Canada. Publication No: 2010-03-E, 3 February 2010. Revised 20 November 2012. [Google Scholar] [Crossref]

9. Luo, X. & Liao, Q. (2007). Awareness Education as the key to Ransomware Prevention. Information Security Journal, 16(4), 195-202. [Google Scholar] [Crossref]

10. Mehdi, S. & Arbi, G. (2104). Security Requirements in Social Networks. Issues in Information Systems Volume 15, Issue I, pp. 81-87. [Google Scholar] [Crossref]

11. Joe, M. &Ramakrishnan, B. (2014). A Survey of Various Security Issues in Online Social Networks. International Journal of Computer Networks and Applications Volume 1, Issue 1, November – December. [Google Scholar] [Crossref]

12. Gangopadhyay, S. & Dhar, D. (2014). Social Networking Sites and Privacy Issues Concerning Youths. Global Media Journal-Indian Edition ISSN 2249 – 5835 Summer Issue/June 2014/Vol. 5/No. 1 [Google Scholar] [Crossref]

13. Okurumeh, O. & Ukaoha, K. (2015) Information Security Issues Surrounding Use of Social Media Networks in Organizations: An Appraisal. Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 6(3): 227- 232 [Google Scholar] [Crossref]

14. Awad, Ali Ismail. (2012). Machine Learning Techniques for Fingerprint Identification: A Short Review.: International Conference on Advanced Machine Learning Technologies and Applications. DOI:10.1007/978-3-642-35326-0_52. [Google Scholar] [Crossref]

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