Phishing URL Identification Using Machine Learning, Ensemble Learning and Deep Learning Techniques-Reference-Cited by-同舟云学术

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Phishing URL Identification Using Machine Learning, Ensemble Learning and Deep Learning Techniques

Published:2022 Issue: Volume: Page:573-582
ISSN:2190-3018
Container-title:Smart Intelligent Computing and Applications, Volume 2
language:
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Author:

Laxmi Prasanna K.,Pradeepthi K. V.,Saxena Ashutosh

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-9705-0_56

Reference8 articles.

1. WooiLiew, S., Sani, N.F.M., Tauk Abdullah, M., Yaakob, R., Sharum, M.Y.: An effective security alert mechanism for real-time phishing tweet detection on Twitter. Comput. Secur. 83, 201–207 (2019)

2. Chiewa, K.L., Tana, C.L., Wong, K.S., Yongc, K.S.C., Tionga, W.K.: A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Inf. Sci. 484, 153–166 (2019)

3. Sahingoz, O.K., Buber, E., Demir, O., Diri, B.: Machine learning based phishing detection from URLs. Expert Syst. Appl. 117, 345–357 (2019)

4. Rao, R.S., Pais, A.R.: Detection of phishing websites using an efficient feature-based machine learning framework. Neural Comput. Appl. 31, 3851–3873 (2018)

5. Makkar, A., Kumar, N., Sama, L., Mishra, S., Samdani, Y.: An intelligent phishing detection scheme using machine learning. In: Proceedings of the Sixth International Conference on Mathematics and Computing, 11 Dec, pp. 151–165, Springer (2020)

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1. An Effective Way to Detect Phishing Website Using Machine Learning;2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT);2024-05-02

2. COVID 19 Patient Recognition and Prevention Utilizing Machine Learning and CNN Model Techniques;2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA);2023-10-07

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