Abstract
The expeditious hike of the Internet has impelled an expansion in the number of malicious URLs. These URLs, when clicked, can infect a computer with malware, steal sensitive information, or redirect to phishing sites. As a result, malicious URL detection has become a crucial aspect of Internet security. We broadly survey techniques and challenges in detecting malicious URLs. In this paper we have used seven classifiers and five boosting algorithms such as Gaussian NB, decision tree, random forest, KNN, AdaBoost, ExtraTrees, gradient boosting, categorical boosting, logic regression, SGD, XGboost, and Light GBM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Maheshwari, S., Janet, B., Kumar, R.: Malicious URL Detection: A Comparative Study, pp. 1147–1151 (2021). https://doi.org/10.1109/ICAIS50930.2021.9396014
Bingi, S.R.: Improving the classification rate for detecting Malicious URL using Ensemble Learning Methods. Master’s thesis, Dublin, National College of Ireland (2021)
Catak, F.O., ÅžahinbaÅŸ, K., Dort Kardes, V.: Malicious URL Detection Using. Machine Learning. (2020). https://doi.org/10.4018/978-1-7998-5101-1.ch008
He, S., Li, B., Peng, H., **n, J., Zhang, E.: An effective cost-sensitive XGBoost method for malicious URLs detection in imbalanced dataset. IEEE Access, 1–1 (2021). https://doi.org/10.1109/ACCESS.2021.3093094
Gbenga, F., Adetunmbi, A., Elohor, O.: Towards optimization of malware detection using extra-tree and random forest feature selections on ensemble classifiers. Int. J. Recent Technol. Eng. 9, 223–232 (2021). https://doi.org/10.35940/ijrte.F5545.039621
Sahoo, D., Liu, C., Hoi, S.: Malicious URL Detection using Machine Learning: A Survey. (2017)
Liu, C., Wang, L., Lang, B., Zhou, Y.: Finding effective classifier for malicious URL detection, 240–244 (2018). https://doi.org/10.1145/3180374.3181352
Ghaleb, F., Alsaedi, M., Saeed, F., Ahmad, J., Alasli, M.: Cyber threat intelligence-based malicious URL detection model using ensemble learning. Sensors. 22 (2022). https://doi.org/10.3390/s22093373
https://www.kaggle.com/code/hamzamanssor/detectionmalicious-url-using-ml-models
https://www.analyticsvidhya.com/blog/2021/04/beginners-guide-to-logistic-regression-using-python/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nair, B.J., Kavya, E., Nandakumar, R. (2024). Comparative Study on Malicious URL Using Classifiers and Boosting Algorithms. In: Gopi, E.S., Maheswaran, P. (eds) Proceedings of the International Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication. MDCWC 2023. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-47942-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-031-47942-7_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47941-0
Online ISBN: 978-3-031-47942-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)