Smart Phishing Detection in Web Pages using Supervised Deep Learning Classification and Optimization Technique ADAM-Reference-Cited by-同舟云学术

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Smart Phishing Detection in Web Pages using Supervised Deep Learning Classification and Optimization Technique ADAM

Published:2021-03-07 Issue:4 Volume:118 Page:3549-3564
ISSN:0929-6212
Container-title:Wireless Personal Communications
language:en
Short-container-title:Wireless Pers Commun

Author:

Lakshmi L.,Reddy M. Purushotham,Santhaiah Chukka,Reddy U. Janardhan

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications

Link

https://link.springer.com/content/pdf/10.1007/s11277-021-08196-7.pdf

Reference25 articles.

1. Buzuraiq, S., Alkasassbeh M., and Almseidin, M. (2020) “Intelligent Methods for Accurately Detecting Phishing Websites,” in 2020 11th International Conference on Information and Communication Systems (ICICS), 085–090 https://doi.org/10.1109/ICICS49469.2020.239509.

2. Adebowale, M. A., Lwin, K. T., Sánchez, E., & Hossain, M. A. (2019). Intelligent web-phishing detection and protection scheme using integrated features of Images, frames and text. Expert Systems with Applications, 115, 300–313. https://doi.org/10.1016/j.eswa.2018.07.067.

3. MohithGowda, H. R., Adithya, M. V., & Vinay S. (2020). Development of anti-phishing browser based on random forest and rule of extraction framework. Cyber Security, 3(1), 1–14. https://doi.org/10.1186/s42400-020-00059-1.

4. Prakash, P., Kumar, M., Kompella, R. R., & Gupta, M., (2010). Phishnet: predictive blacklisting to detect phishing attacks. In 2010 proceedings IEEE INFOCOM (pp. 1–5). https://doi.org/10.1109/INFCOM.2010.5462216.

5. Basit, A., Zafar, M., Liu, X., Javed, A. R., Jalil, Z., & Kifayat, K. (2020). A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommunication System. https://doi.org/10.1007/s11235-020-00733-2.

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