Authors : G Ramana Reddy; Dr. Vara Lakshmi Thavva; L. Sainath Yadav; Dr. K Jagannayaki

Volume/Issue : Volume 10 - 2025, Issue 4 - April

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Google Scholar : https://tinyurl.com/yx9vt2br

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DOI : https://doi.org/10.38124/ijisrt/25apr586

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Abstract : In the digital age, social media has emerged as a powerful tool for businesses to connect with consumers, build brand identity, and drive marketing strategies. This paper explores the impact and effectiveness of leveraging social media platforms—such as Facebook, Instagram, Twitter, LinkedIn, and TikTok—for marketing and brand awareness. It highlights how targeted content, influencer collaborations, real-time engagement, and data analytics contribute to brand visibility and customer loyalty. The study also examines case examples of successful social media campaigns and outlines best practices for maximizing reach and engagement. By understanding the dynamics of social media marketing, businesses can create more authentic, interactive, and results-driven campaigns, ultimately fostering stronger brand- consumer relationships in an increasingly connected world.

Keywords : Social Media, Content Strategy, Brand Identity, Influencer Marketing.

References :

1. Yan Shujun, Wang Wenjie, Zhang Yuqing,2019, An effective webfingerprint identification method Journal of the Chinese Academy of Sciences, 33(05): 679- 685.
2. Dukes L S, Yuan X, Akowuah F.2018 A case study on web application security testing with tools manual testing[C]//Southeast, Proceedings of IEEE. IEEE, 2014:1-6.
3. Huang Z, Xia C, Sun B, et al. Analyzing and summarizing the webserver detection technology based on HTTP[C]//Software Engineering and Service Science (ICSESS), 2016.
4. Goethem T V, Chen P, Nikiforakis N, et al. Large-scale security analysis of the web: challenges and findings[J]. Lecture Notes in Computer Science, 2016, 8564:110-126.
5. Ma Cheng. Principle Research and Security Application of Cyberspace Search Engine [J]. Cyberspace Security, 2016, 7(05): 6-10.
6. Zhao Jianjun. Research on Identification Technology of Network Space Terminal Equipment [D]. Lanzhou Technology University, 2016.
7. Chen Zhuo. Design and implementation of scanning platform for industrial control system based on stateless connection [D]. Beijing University of Posts and Telecommunications, 2015.REFERENCES
8. Lu Wendi, Li Xiaohui, Wang Han. Research and Implementationof Security Access Method for Internet of Things Based on Device Fingerprint [J]. Electronic Design Engineering, 201, 27(03): 136- 141.
9. Huang Z, Xia C, Sun B, et al. Analyzing and summarizing the web server detection technology based on HTTP[C]//Software Engineering and Service Science (ICSESS), 2015 6th IEEE International Conference on. IEEE, 2020:1042-1045.
10. Karthik R, Kamath S. W3-Scrape-A windows-based reconnaissance tool for web application fingerprinting[R]. arXiv:1306.6839.
11. Kozina M, Golub M, Groš S. A method for identifying Web applications[J]. International Journal of Information Security,2014,8(6):455-467.
12. Lee D, Rowe J, Ko C, et al. Detecting and defending against Web-server fingerprinting[C]//Proceedings of Computer Security Applications Conference, 2002. 18th Annual. IEEE, 2021:321-330.
13. Atana worabhan S, Livshits B, Zorn BG. Noxes: A client- side solution for mitigating cross-site scripting attacks. In Proceedings of the Network and Distributed System Security Symposium (NDSS), 2006.
14. BGP by cautiously adopting routes. In Proceedings of the ACM SIGCOMM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2003.
15. Karasaridis A, Rexford J. Pretty Good BGP: Improving BGP by cautiously adopting routes. In Proceedings of the ACM SIGCOMM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2003.
16. Chatzimisios P, Boucouvalas AC, Vitsas V. Security issues in mobile ad hoc networks— a survey. International Journal of Communication Systems, 2003.
17. Holz T, Gorecki C, Rieck K, Freiling FC. Measuring and detecting fast-flux service networks. In Proceedings of the 1st USENIX Workshop on Large-Scale Exploits and Emergent Threats, 2002.
18. Maniraj, S. P., Saini, A., Ahmed, S., & Sarkar, S. D. Credit Card Fraud Detection using Machine Learning and Data Science. International Journal of Engineering Research & Technology (IJERT), 8(09).
19. Dubey, S., Mundhe, K., & Kadam, A. (2001). Credit Card Fraud Detection using Artificial Neural Network and BackPropagation. Proceedings of 4th International Conference on Intelligent Computing and Control Systems, Madurai.
20. Yu, X., Li, X., Dong, Y., & Zheng, R. (2000). A Deep Neural Network Algorithm for Detecting Credit Card Fraud. International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Fuzhou, China, 181-183.
21. Jebaseeli, T. Jemima, Venkatesan, R., & Ramalakshmi, K. (2000). Fraud detection for credit card transactions using random forest algorithm. Intelligence in Big Data Technologies—Beyond the Hype, Springer, Singapore, 189- 197.