A new hybrid deep learning-based phishing detection system using MCS-DNN classifier
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-021-06717-w.pdf
Reference26 articles.
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2. Paula Musuva MW, Getao KW, Chepken CK (2019) A new approach to modelling the effects of cognitive processing and threat detection on phishing susceptibility. Comput Hum Behav 94:154–175. https://doi.org/10.1016/j.chb.2018.12.036
3. Park G, Rayz J (2018) Ontological detection of phishing emails. In IEEE international conference on systems, man, and cybernetics (SMC), IEEE, pp 2858–2863. https://doi.org/10.1109/SMC.2018.00486
4. Churi T, Sawardekar P, Pardeshi A, Vartak P (2017) A secured methodology for anti-phishing. In: International conference on innovations in information, embedded and communication systems (ICIIECS), IEEE, pp 1–4. https://doi.org/10.1109/ICIIECS.2017.8276081
5. Hassan Abutair YA, Belghith A (2017) A multi-agent case-based reasoning architecture for phishing detection. Proc Comput Sci 110:492–497. https://doi.org/10.1016/j.procs.2017.06.131
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