DETECTING AND EVALUATING FAKE WEBSITES USING PATTERN RECOGNITION ALGORITHMS
Keywords:
web Security, Machine Learning, Random Forest, Cyberattacks, Fake Websites, URLAbstract
The increase in the creation of fake web pages by attackers is leading to a sharp increase in cyberattacks. Attackers use these fake web sites to advertise products to Internet users, distribute malicious programs, or steal users' valuable logins and passwords. Traditional solutions for detecting such fake web addresses are not effective in detecting newly created fake web addresses. In this article, we propose a new approach that combines several machine learning algorithms. It can be seen that the Random Forest classifier (accuracy of 99%) can be considered more reliable than the others in detecting fake web addresses.
References
Singla, S., Gandotra, E., Bansal, D., & Sofat, S. “A novel approach to malware detection using static classification”, International Journal of Computer Science and Information, Vol.13, No.3, pp.1-5, 2015.
Enterprise, V. "Verizon 2018 data breach investigations report", 2018. [Online]. Available: https://verizon.com/business/resources/reports/2018 -data-breach-digest.pdf
Gandotra, E., Bansal, D., & Sofat, S. “Malware intelligence: beyond malware analysis”, International Journal of Advanced Intelligence Paradigms, Vol.13, No.1-2, pp.80-100, 2019. DOI: 10.1504/IJAIP.2019.099945
Sharma, A., Gandotra, E., Bansal, D., & Gupta, D. “Malware capability assessment using fuzzy logic”, Cybernetics and Systems, Vol.50, No.4, pp.323-338, 2019. DOI: 10.1080/01969722.2018.1552906
Chiew, K.L., Yong, K.S.C., & Tan, C.L.J.E.S.w.A. “A survey of phishing attacks: Their types, vectors, and technical approaches”, Expert Systems with Applications, Vol.106, pp.1-20, 2018. DOI: 10.1016/j.eswa.2018.03.050
Gandotra, E., & Sofat, S.J.I.J.o.N.-G.C. “Tools & Techniques for Malware Analysis and Classification”, International Journal of Next-Generation Computing, Vol.7, No.3, pp.176-197, 2016.
Chiew, K. L., Chang, E. H., & Tiong, W. K. “Utilization of website logo for phishing detection”, Computers & Security, Vol.54, pp.16-26, 2015. DOI: 10.1016/j.cose.2015.07.006
AlSabah, M., Nabeel, M., Boshmaf, Y., & Choo, E. “Content-Agnostic Detection of Phishing Domains Using Certificate Transparency and Passive DNS”, Proceedings of the 25th International Symposium on Research in Attacks, Intrusions, and Defenses, pp. 446-459, 2022. DOI: 10.1145/3545948.3545958
Torrealba A, L. and Bustos-Jiménez, J. “Detecting Phishing in a Heuristic Way (Abstract)”, 2021.
Sinha, J., & Sachan, M. “PhishX: An Empirical Approach to Phishing Detection”, 2022. DOI: 10.1145/1122445.1122456
Ranaldi, L., Petito, M., Gerardi, M., Fallucchid, F., & Zanzotto, F.M. “Machine Learning Techniques for Italian Phishing Detection”, in Italian Conference on Cybersecurity, Rome, Italy 2022.
A.Kabulov, I. Yarashov and A. Otakhonov, "Algorithmic Analysis of the System Based on the Functioning Table and Information Security," 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), Toronto, ON, Canada, 2022, pp. 1-5, doi: 10.1109/IEMTRONICS55184.2022.9795746.
Normatov, I., Yarashov, I., Otakhonov, A., & Ergashev, B. (2022, September). Construction of reliable well distribution functions based on the principle of invariance for convenient user access control. In 2022 International Conference on Information Science and Communications Technologies (ICISCT) (pp. 1-5). IEEE.
Toshmatov, S., Yarashov, I., Otakhonov, A., & Ismatillayev, A. (2022, September). Designing an algorithmic formalization of threat actions based on a Functioning table. In 2022 International Conference on Information Science and Communications Technologies (ICISCT) (pp. 1-5). IEEE.
Additional Files
Published
How to Cite
License
Copyright (c) 2025 Анвар Кабулов, Алишербек Отахонов
This work is licensed under a Creative Commons Attribution 4.0 International License.
