Detection of Malicious URLs Using Gradient Boosting Classifier-Reference-Cited by-同舟云学术

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Detection of Malicious URLs Using Gradient Boosting Classifier

Published:2023 Issue: Volume: Page:329-339
ISSN:2190-3018
Container-title:Evolution in Computational Intelligence
language:
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Author:

Sultana Saba,Reddy Madhavi K.,Lavanya G.,Swarna Latha J.,Sandhyarani ,Prathyusha Balijapalli

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-6702-5_28

Reference14 articles.

1. Aljabri, M., Alhaidari, F., Mohammad, R.M.A., Mirza, S., Alhamed, D.H., Altamimi, H.S., Chrouf, S.M.B.: An assessment of lexical, network, and content-based features for detecting malicious URLs using machine learning and deep learning models. Comput. Intell. Neurosci. 2022, 1–14 (2022). https://doi.org/10.1155/2022/3241216

2. Johnson, C., Khadka, B., Basnet, R.B., Doleck, T.: Towards detecting and classifying malicious URLS using deep learning. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 11(4), 31–48 (2020). https://doi.org/10.22667/JOWUA.2020.12.31.031

3. Yuan, J., Chen, G., Tian, S., Pei, X.: Malicious URL detection based on a parallel neural joint model. IEEE Access 9, 9464–9472 (2021). https://doi.org/10.1109/ACCESS.2021.3049625

4. Rao, R.S., Pais, A.R., Anand, P.: A heuristic technique to detect phishing websites using TWSVM classifier’. Neural Comput. Appl. 33(11), 5733–5752 (2021). https://doi.org/10.1007/s00521-020-05354-z

5. Vinayakumar, R., Soman, K.P., Poornachandran, P.: Evaluating deep learning approaches to characterize and classify malicious URL’s. J. Intell. Fuzzy Syst. 34(3), 1333–1343 (2018). https://doi.org/10.3233/JIFS-169429

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1. Utilizing Ensemble Learning to enhance the detection of Malicious URLs in the Twitter dataset;2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM);2024-02-21

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