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- Vol. 6 Issue 6
- Article
AI-Based Cyber Threat Detection System: A Hybrid Machine Learning and AI Reasoning Approach for Real-Time Malicious URL Detection
Authors
1 Associate Professor, Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology Davangere, Karnataka, India. 2 3 4 5 Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology Davangere, Karnataka, India.
Article Information
- Published In
- Volume 6, Issue 6 (2026)
- Published Online
- November-December 2026
- Pages
- 50-55
- Article Type
- Original Article
References
1. Anti-Phishing Working Group (APWG), "Phishing activity trends report." [Online]. Available: https://apwg.org/trendsreports/
2. O. K. Sahingoz, E. Buber, O. Demir, and B. Diri, "Machine learning based phishing detection from URLs," Expert Syst. Appl., vol. 117, pp. 345–357, 2019.
3. H. Ghalechyan, E. Israyelyan, A. Arakelyan, G. Hovhannisyan, and A. Davtyan, “Phishing URL detection with neural networks: An empirical study,” Scientific Reports, vol. 14, Art.no. 25134, 2024, doi: 10.1038/s41598-024-74725-6.
4. N. F. Almujahid, M. A. Haq, and M. Alshehri, “Comparative evaluation of machine learning algorithms for phishing site detection,” PeerJ Computer Science, vol. 10, Art. no. e2131, 2024, doi: 10.7717/peerj-cs.2131.
5. S. Gopali, A. S. Namin, F. Abri, and K. S. Jones, “The performance of sequential deep learning models in detecting phishing websites using contextual features of URLs,” in Proc. 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), 2024, pp. 1064–1066, doi: 10.1145/3605098.3636164.
6. L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
7. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining (KDD '16), San Francisco, CA, USA, 2016, pp. 785–794.
8. F. Pedregosa et al., "Scikit-learn: Machine learning in Python," J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
9. A. Niculescu-Mizil and R. Caruana, "Predicting good probabilities with supervised learning," in Proc. 22nd Int. Conf. Mach. Learn. (ICML '05), Bonn, Germany, 2005, pp. 625–632.
10. Google AI for Developers, "Gemini API documentation." [Online]. Available: https://ai.google.dev/gemini-api/docs
11. FastAPI, "FastAPI documentation." [Online]. Available:
2. O. K. Sahingoz, E. Buber, O. Demir, and B. Diri, "Machine learning based phishing detection from URLs," Expert Syst. Appl., vol. 117, pp. 345–357, 2019.
3. H. Ghalechyan, E. Israyelyan, A. Arakelyan, G. Hovhannisyan, and A. Davtyan, “Phishing URL detection with neural networks: An empirical study,” Scientific Reports, vol. 14, Art.no. 25134, 2024, doi: 10.1038/s41598-024-74725-6.
4. N. F. Almujahid, M. A. Haq, and M. Alshehri, “Comparative evaluation of machine learning algorithms for phishing site detection,” PeerJ Computer Science, vol. 10, Art. no. e2131, 2024, doi: 10.7717/peerj-cs.2131.
5. S. Gopali, A. S. Namin, F. Abri, and K. S. Jones, “The performance of sequential deep learning models in detecting phishing websites using contextual features of URLs,” in Proc. 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), 2024, pp. 1064–1066, doi: 10.1145/3605098.3636164.
6. L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
7. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining (KDD '16), San Francisco, CA, USA, 2016, pp. 785–794.
8. F. Pedregosa et al., "Scikit-learn: Machine learning in Python," J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
9. A. Niculescu-Mizil and R. Caruana, "Predicting good probabilities with supervised learning," in Proc. 22nd Int. Conf. Mach. Learn. (ICML '05), Bonn, Germany, 2005, pp. 625–632.
10. Google AI for Developers, "Gemini API documentation." [Online]. Available: https://ai.google.dev/gemini-api/docs
11. FastAPI, "FastAPI documentation." [Online]. Available:
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