EFFICIENT E-MAIL PHISHING DETECTION USING MACHINE LEARNING

Authors

  • Mr.R.Sreedhar, Perumandla Sushma, N.Sanjana, K.Pranuthi kavya

Keywords:

RCNN ,CFI, RMSEA, NFI, Minnesota living with heart failure questionnaire, Heart Failure , Factor analysis.

Abstract

Phishing messages are a critical worldwide danger, causing significant monetary misfortunes. In spite of continuous updates to balance these dangers, the aftereffects of current techniques stay unacceptable, particularly as phishing messages have been expanding alarmingly lately. Along these lines, more successful phishing discovery advancements are expected to relieve this danger.

Downloads

Download data is not yet available.

References

A. Aleroud and L. Zhou, "Phishing environments, techniques, and countermeasures: A survey," Computers & Security, vol. 68, pp. 160-196, 2017.

I. Vayansky and S. Kumar, "Phishing challenges and solutions," Computer Fraud & Security, vol. 2018, pp. 15-20,2018.

Downloads

Published

2023-12-24

How to Cite

Mr.R.Sreedhar, Perumandla Sushma, N.Sanjana, K.Pranuthi kavya. (2023). EFFICIENT E-MAIL PHISHING DETECTION USING MACHINE LEARNING. Pegem Journal of Education and Instruction, 13(4), 598–606. Retrieved from https://pegegog.net/index.php/pegegog/article/view/4025

Issue

Vol. 13 No. 4 (2023): PEGEGOG

Section

Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

NonCommercial — You may not use the material for commercial purposes.

No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.