Authors : Tasneem A. Bandahala; Nur-Sheba S. Suhaili; Kyla A. Monabi; Herni K. Suhuri; Sitti Nelsa Y. Iboh; Mershaida M. Jaujali; Nursina E. Bagindah; Munralina A. Musin; Nurmaida A. Shaik; Kirnihar Adjaraini; Shernahar K. Tahil; Nureeza J. Latorre

Volume/Issue : Volume 10 - 2025, Issue 1 - January

Google Scholar : https://tinyurl.com/3jdc7ysu

Scribd : https://tinyurl.com/2e34pjae

DOI : https://doi.org/10.5281/zenodo.14621440

Abstract : Phishing emails pose a significant threat to individuals and organizations, often serving as the gateway for data breaches, financial losses, and compromised security. Traditional defense mechanisms, while essential, struggle to combat the growing sophistication and volume of phishing attacks. Artificial Intelligence (AI) has emerged as a transformative solution, enhancing email security through advanced detection and prevention techniques. By employing machine learning (ML) algorithms and natural language processing (NLP), AI can analyze email content, sender behavior, and metadata to identify phishing attempts with remarkable precision. Unlike static rule-based systems, AI adapts to evolving threats, detecting even previously unseen phishing tactics. Real- time analysis and automated threat response further bolster its effectiveness, reducing reliance on human intervention and minimizing errors. This paper examines the role of AI in combating phishing emails, discussing its methods, advantages, and limitations. It also explores how AI-powered solutions are shaping the future of email security, providing organizations with a robust defense against cyber threats. As the battle against phishing intensifies, AI stands at the forefront, offering a proactive and dynamic approach to safeguarding digital communication.

References :

1. Bhowmick, S., & Hazarika, B. B. (2021). Detection of phishing emails using machine learning approaches: A survey. Cybersecurity, 4(1), 1-25.
2. Verma, R., & Hossain, N. (2017). Semantic feature selection for phishing email detection. Computers & Security, 65, 307-324.
3. Ting, S. L., Tse, Y. K., & Ho, G. T. (2018). Artificial intelligence-based phishing detection systems: A review. Expert Systems with Applications, 97, 260-272.
4. Gupta, B. B., Arachchilage, N. A. G., & Psannis, K. E. (2017). Defending against phishing attacks: Taxonomy of methods, current issues, and future directions. Telecommunication Systems, 67(2), 247-267.
5. Al-Mohannadi, H., & Johnson, P. (2020). Using natural language processing to combat phishing attacks in email communication. Cybersecurity Journal, 3(4), 15-29.
6. Jagatic, T. N., Johnson, N. A., Jakobsson, M., & Menczer, F. (2007). Social phishing: Understanding the effectiveness of phishing attacks using social networks. Communications of the ACM, 50(10), 94-100.
7. Nikolai, P., & Wagner, S. (2021). Phishing detection using artificial neural networks: Challenges and opportunities. International Journal of Computer Science, 10(2), 45-59.
8. Google AI Blog (2023). Machine learning advancements in phishing detection. Retrieved from Google AI Blog.
9. Symantec Corporation (2022). Phishing threats in a digital era: The role of AI and ML. Retrieved from Symantec.
10. IBM Security (2023). Using AI to stay ahead of phishing attacks. Retrieved from IBM Security Blog.
11. Ahmed, I. (2019). A Survey on Phishing Email Detection Using Machine Learning. International Journal of Advanced Computer Science and Applications, 10(3), 442-453.
12. Chiew, K. L. (2020). Phishing Email Detection using Machine Learning and Deep Learning. Journal of Intelligent Information Systems, 57(2), 247-262.
13. Duman, E. (2019). Phishing Detection Using Machine Learning and Natural Language Processing. Journal of Information Security and Applications, 44, 102924.
14. Jain, A. K. (2018). Phishing Email Detection: A Machine Learning Approach. International Journal of Cybersecurity Intelligence and Cyberforensics, 2(1), 1-13.
15. Khan, Z. (2020). A Review on Phishing Email Detection Techniques Using Machine Learning. Journal of Cybersecurity and Information Systems, 4(1), 1-12.