AI-Driven Techniques for Detecting and Preventing Social Media Phishing Attacks
Keywords:
Social Media Phishing, AI-Driven Detection, Machine Learning, Cybersecurity, Real-Time Prevention
Abstract
This study delves into AI-powered methods for identifying and avoiding social media phishing assaults, with an emphasis on building a strong machine-learning model that accomplishes impressive performance measures. The model's 95% accuracy, 92% precision, and 90% recall guarantee accurate phishing threat classification with few false positives. Optimal response times are another element of the system that makes it suitable for real-time use in dynamic settings like social media. As the dangers of social media phishing continue to rise, the findings show that AI-based solutions could improve cybersecurity measures in a scalable and effective way.
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2. Yadav, Prashant. "Digital transformation in the health product supply chain: a framework for analysis." Health Systems & Reform 10, no. 2 (2024): 2386041.
3. Putra, Fauzan Prasetyo Eka, Achmad Zulfikri, Goffal Arifin, and Revi Mario Ilhamsyah. "Analysis of Phishing Attack Trends, Impacts and Prevention Methods: Literature Study." Brilliance: Research of Artificial Intelligence 4, no. 1 (2024): 413-421.
4. Salem, Aya H., Safaa M. Azzam, O. E. Emam, and Amr A. Abohany. "Advancing cybersecurity: a comprehensive review of AI-driven detection techniques." Journal of Big Data 11, no. 1 (2024): 105.
5. Nagar, Gourav. "The Evolution of Ransomware: Tactics, techniques, and mitigation strategies." International Journal of Scientific Research and Management (IJSRM) 12, no. 06 (2024): 1282-1298.
6. Alabdan, Rana. "Phishing attacks survey: Types, vectors, and technical approaches." Future internet 12, no. 10 (2020): 168.
7. Alabdan, Rana. "Phishing attacks survey: Types, vectors, and technical approaches." Future internet 12, no. 10 (2020): 168.
8. Nadeem, Muhammad, Syeda Wajiha Zahra, Muhammad Nouman Abbasi, Ali Arshad, Saman Riaz, and Waqas Ahmed. "Phishing attack, its detections and prevention techniques." International Journal of Wireless Security and Networks 1, no. 2 (2023): 13-25p.
9. Nadeem, Muhammad, Syeda Wajiha Zahra, Muhammad Nouman Abbasi, Ali Arshad, Saman Riaz, and Waqas Ahmed. "Phishing attack, its detections and prevention techniques." International Journal of Wireless Security and Networks 1, no. 2 (2023): 13-25p.
10. Kaushik, Priyanka, and Saurabh Pratap Singh Rathore. "Deep Learning Multi-Agent Model for Phishing Cyber-attack Detection." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (2023): 680-686.
11. San San, Smart Elizabeth, and Temitope OLajumoke. "AI-Driven Risk Management in Information Security: Harnessing Predictive Analytics and Automation to Enhance Protection."
12. Fatma, Sabiha. "AI-Based Social Engineering Detection."
13. Gaioto, Fiza. "Innovations in Phishing Defense: Combining Supervised Classifiers in AI with Nature-Inspired Algorithms for Enhanced Security." (2023).
14. babu Nuthalapati, Suri. "AI-enhanced detection and mitigation of cybersecurity threats in digital banking." Educ. Adm. Theory Pract. 29, no. 1 (2023): 357-368.
15. Vaahersalo, Jack. "Leveraging Big Data for Intrusion Detection and Phishing Defense: Innovations in Machine Learning Ensembles and AI Supervised Classifiers." (2023).
16. Ali, Hamid. "AI-Powered Supervised Classifiers in Big Data Environments for Phishing Defense and Intrusion Detection." (2022).
17. Biggio, Battista, Igino Corona, Blaine Nelson, Benjamin IP Rubinstein, Davide Maiorca, Giorgio Fumera, Giorgio Giacinto, and Fabio Roli. "Security evaluation of support vector machines in adversarial environments." Support vector machines applications (2014): 105- 153.
Published
2024-11-14
How to Cite
Vibha Sharma, & Dr. Manish Kumar Goyal. (2024). AI-Driven Techniques for Detecting and Preventing Social Media Phishing Attacks. Revista Electronica De Veterinaria, 25(2), 1887-1896. https://doi.org/10.69980/redvet.v25i2.1889
Section
Articles
