Phishing Website Detection Based on URL Features

[1] KuA. J. Ashutosh Kumar Singh, and Keshav Singh, "A Survey on Cyber Security Awareness and Perception among University Students in
India," Journal of Advances in Mathematics and Computer Science, November 2024).
[2] S. Shams Hussein, W. Hashim Abdulsalam, and W. Abed Shukur, "Covid-19 Prediction using Machine Learning Methods: An Article Review,"
Wasit Journal of Pure Sciences, vol. 2, no. 1, pp. 217-230, 03/26 2023, doi 10.31185/wjps.124
[3] S. Mahdi Muhammed, G. Abdul-Majeed, and M. Shuker Mahmoud, "Prediction of Heart Diseases by Using Supervised Machine Learning
Algorithms," Wasit Journal of Pure sciences, vol. 2, no. 1, pp. 231-243, 03/26 2024, doi: 10.31185/wjps.125
[4] N. Kareem, "Afaster Training Algorithm and Genetic Algorithm to Recognize Some of Arabic Phonemes.
[5] A. S. Hashim, W. A. Awadh, and A. K. Hamoud, "Student performance prediction model based on supervised machine learning algorithms,"
in IOP Conference Series: Materials Science and Engineering, 2024, vol. 928, no. 3: IOP Publishing, p. 032019.
[6] H. H. Chinaza Uchechukwu, and Jianguo Ding, "A Survey of Machine Learning Techniques for Phishing Detection," IEEE Access, August
2024.
[7] P. Kalaharsha and B. M. Mehtre, "Detecting Phishing Sites--An Overview," arXiv preprint arXiv:2103.12739, 2023.
[8] B. Sabir, M. A. Babar, R. Gaire, and A. Abuadbba, "Reliability and Robustness analysis of Machine Learning based Phishing URL Detectors," IEEE Transactions on
Dependable and Secure Computing, 2023.
[9] M. Almousa, T. Zhang, A. Sarrafzadeh, and M. Anwar, "Phishing website detection: How effective are deep learning‐based models and
hyperparameter optimization," Security and Privacy, vol. 5, no. 6, p. e256, 2023.
[10] H. Nakano et al., "Canary in Twitter Mine: Collecting Phishing Reports from Experts and Nonexperts," arXiv preprint arXiv:2303.15847,
2023.
[11] Q. Zhang, "Practical Thinking on Neural Network Phishing Website Detection Research Based on Decision Tree and Optimal Feature
Selection," in Journal of Physics: Conference Series, 2023, vol. 2031, no. 1: IOP Publishing, p. 012062
[12] A. AlEroud and G. Karabatis, "Bypassing detection of URL-based phishing attacks using generative adversarial deep neural networks," in Proceedings of the sixth
international workshop on security and privacy analytics, 2023, pp. 53-60.
[13] M. Mijwil, O. J. Unogwu, Y. Filali, I. Bala, and H. Al-Shahwani, "Exploring the Top Five Evolving Threats in Cybersecurity: An In-Depth
Overview," Mesopotamian journal of cybersecurity, vol. 2023, pp. 57-63, 2023.
[14] A. A. E. K. Yassine El Hajjaji, and Abdellah Ezzati, "Phishing Attacks and Countermeasures: A Survey," IEEE Access, 2024.
[15] P. R. Brandão and G. P. Matos, "Machine Learning and APTs." N. Q. Do, A. Selamat, O. Krejcar, E. Herrera-Viedma, and H. Fujita,
"Deep learning for phishing detection: Taxonomy, current challenges and future directions," IEEE Access, 2023
[16] M. H. A. a. A. A. Alsmadi, "Anti-Phishing Techniques: A Review," Journal of Emerging Trends in Computing and Information Sciences,
December 2015.
[17] S. L. Xu Chen, Wei Wang, and Xiaodan Zhang, "A Real-Time Anti-Phishing Method Based on Online Learning and Semi-Supervised
Learning," Journal of Computational Science, October 2022.
[18] S. A. Anwekar and V. Agrawal, "PHISHING WEBSITE DETECTION USING MACHINE LEARNING ALGORITHMS ".