INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
One of the biggest risks in the modern world is phishing emails, which have resulted in enormous financial losses. The current state of confrontation methods is not very satisfactory, even though they are continually being upgraded. Furthermore, the number of phishing emails has been alarmingly rising in recent years. Thus, to lessen the danger posed by phishing emails, more sophisticated phishing detection technology is required. First, we examined an email's structure in this essay. Next, we introduced a new phishing email detection model termed, which is used to simultaneously model emails at the character and word levels in the email body and email header. This model is based on the enhanced Recurrent Convolutional Neural Networks (RCNN) model with multilevel vectors and attention mechanism. To assess efficacy, we employ an imbalanced dataset.
"Rcnn Based Phishing Email Detection Using Deep Learning Techniques-Django Framework", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 4, page no.741 - 748, April-2024, Available :http://www.ijsdr.org/papers/IJSDR2404103.pdf
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Publication Details:
Published Paper ID: IJSDR2404103
Registration ID:210762
Published In: Volume 9 Issue 4, April-2024
DOI (Digital Object Identifier):
Page No: 741 - 748
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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