References
- Perwej, Y., S. Q. Abbas, J. P. Dixit, N. Akhtar, A. K. Jaiswal. A Systematic Literature Review on the Cyber Security. – International Journal of Scientific Research and Management, Vol. 9, 2021, No 12, pp. 669-710.
- Veprytska, O., V. Kharchenko, O. Illiashenko. Cybersecurity and Artificial Intelligence: Triad-Based Analysis and Attacks Review. – Cybernetics and Information Technologies, Vol. 25, 2025, No 3, pp. 156-185.
- Anti-PhishingWorkingGroup (APWG). Phishing Activity Trends Report, 1st Quarter 2025. APWG, 2025.
- Basit, A., M. Zafar, X. Liu, A. R. Javed, Z. Jalil, K. Kifayat. A Comprehensive Survey of AI-Enabled Phishing Attack Detection Techniques. – Telecommunication Systems, Vol. 76, 2021, No 1, pp. 139-154.
- Zonyfar, C., J.-B. Lee, J.-D. Kim. HCNN-LSTM: Hybrid Convolutional Neural Network with Long Short-Term Memory Integrated for Legitimate Web Prediction. – Journal of Web Engineering, Vol. 22, 2023, No 5, pp. 757-782.
- Shirazi, H., S. R. Muramudalige, I. Ray, A. P. Jayasumana, H. Wang. Adversarial Autoencoder Data Synthesis for Enhancing Machine Learning-Based Phishing Detection Algorithms. – IEEE Transactions on Services Computing, Vol. 16, 2023, No 4, pp. 2411-2422.
- Soni, U., A. G. Jethava. Latest Advancements in Credit Risk Assessment with Machine Learning and Deep Learning Techniques. – Cybernetics and Information Technologies, Vol. 24, 2024, No 4, pp. 22-44.
- Priya, S., S. Selvakumar, R. L. Velusamy. PaSOFuAC: Particle Swarm Optimization-Based Fuzzy Associative Classifier for Detecting Phishing Websites. – Wireless Personal Communications, Vol. 125, 2022, No 1, pp. 755-784.
- Nowroozi, E., M. Mohammadi, M. Conti. An Adversarial Attack Analysis on a Malicious Advertisement URL Detection Framework. – IEEE Transactions on Network and Service Management, Vol. 20, 2022, No 2, pp. 1332-1344.
- Feng, T., C. Yue. Visualizing and Interpreting RNN Models in URL-Based Phishing Detection. – In: Proc. of 25th ACM Symposium on Access Control Models and Technologies, ACM, 2020, pp. 13-24.
- Bahnsen, A. C., E. C. Bohorquez, S. Villegas, J. Vargas, F. A. González. Classifying Phishing URLs Using Recurrent Neural Networks. – In: Proc. of 2017 APWG Symposium on Electronic Crime Research (eCrime), IEEE, 2017, pp. 1-8.
- Zhang, X., J. Zhao, Y. LeCun. Character-Level Convolutional Networks for Text Classification. – Advances in Neural Information Processing Systems, Vol. 28, 2015.
- Opara, C., B. Wei, Y. Chen. HTMLPhish: Enabling Phishing Web Page Detection by Applying Deep Learning Techniques on HTML Analysis. – In: Proc. of International Joint Conference on Neural Networks (IJCNN’20), IEEE, 2020, pp. 1-8.
- Korkmaz, M., E. Kocyigit, O. Sahingoz, B. Diri. A Hybrid Phishing Detection System Using Deep Learning-Based URL and Content Analysis. – Elektronika ir Elektrotechnika, Vol. 28, 2022, No 5.
- Dawabsheh, A., M. Jazzar, A. Eleyan, T. Bejaoui, S. Popoola. An Enhanced Phishing Detection Tool Using Deep Learning from URL. – In: Proc. of International Conference on Smart Applications, Communications and Networking (SmartNets’22), IEEE, 2022, pp. 1-6.
- Zhu, E., Q. Yuan, Z. Chen, X. Li, X. Fang. CCBLA: A Lightweight Phishing Detection Model Based on CNN, BiLSTM, and Attention Mechanism. – Cognitive Computation, Vol. 15, 2023, No 4, pp. 1320-1333.
- Hussain, M., C. Cheng, R. Xu, M. Afzal. CNN-Fusion: An Effective and Lightweight Phishing Detection Method Based on a Multi-Variant ConvNet. – Information Sciences, Vol. 631, 2023, pp. 328-345.
- Opara, C., Y. Chen, B. Wei. Look Before You Leap: Detecting Phishing Web Pages by Exploiting Raw URL and HTML Characteristics. – Expert Systems with Applications, Vol. 236, 2024, 121183.
- Prasad, Y. B., V. Dondeti. PDSMV3-DCRNN: A Novel Ensemble Deep Learning Framework for Enhancing Phishing Detection and URL Extraction. – Computers & Security, Vol. 148, 2025, 104123.
- Nayak, G. S., B. Muniyal, M. C. Belavagi. Enhancing Phishing Detection: A Machine Learning Approach with Feature Selection and Deep Learning Models. – IEEE Access, 2025.
- Geng, Y., Q. Li, G. Yang, W. Qiu. Logistic Regression. – In: Practical Machine Learning Illustrated with KNIME, Springer, 2024, pp. 99-132.
- Zou, X., Y. Hu, Z. Tian, K. Shen. Logistic Regression Model Optimization and Case Analysis. – In: Proc. of 7th IEEE International Conference on Computer Science and Network Technology (ICCSNT’19), IEEE, 2019, pp. 135-139.
- Sun, Z., G. Wang, P. Li, H. Wang, M. Zhang, X. Liang. An Improved Random Forest Based on the Classification Accuracy and Correlation Measurement of Decision Trees. – Expert Systems with Applications, Vol. 237, 2024, 121549.
- Zhu, E., Z. Chen, J. Cui, H. Zhong. MOE/RF: A Novel Phishing Detection Model Based on a Revised Multiobjective Evolution Optimization Algorithm and Random Forest. – IEEE Transactions on Network and Service Management, Vol. 19, 2022, No 4, pp. 4461-4478.
- Salman, H. A., A. Kalakech, A. Steiti. Random Forest Algorithm Overview. – Babylonian Journal of Machine Learning, Vol. 2024, 2024, pp. 69-79.
- Ikram, S. T., A. K. Cherukuri, B. Poorva, P. S. Ushasree, C. Zhang, X. Liu, G. Li. Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models. – Cybernetics and Information Technologies, Vol. 21, 2021, No 3, pp. 175-188.
- Ige, A. O., M. Sibiya. State-of-the-Art in 1D Convolutional Neural Networks: A Survey. – IEEE Access, 2024.
- Ma’arif, A., W. Rahmaniar, H. I. K. Fathurrahman, A. Z. K. Frisky. Understanding of Convolutional Neural Network (CNN): A Review. – International Journal of Robotics & Control Systems, Vol. 2, 2022, No 4.
- Alshingiti, Z., R. Alaqel, J. Al-Muhtadi, Q. E. U. Haq, K. Saleem, M. H. Faheem. A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN. – Electronics, Vol. 12, 2023, No 1, 232.
- Soydaner, D. Attention Mechanism in Neural Networks: Where it Comes and Where it Goes. – Neural Computing and Applications, Vol. 34, 2022, No 16, pp. 13371-13385.
- Mahajan, S. 6.5 Lakh URLs Labelled as 1 and 0. Kaggle Dataset. https://www.kaggle.com/datasets/somanshumahajan/65-lakh-urls-labelled-as-1-and-0/data.