UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 9 Issue 5
May-2022
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2205870


Registration ID:
402073

Page Number

h525-h530

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Title

PHISHING WEBSITE DETECTION USING DEEP LEARNING FRAMEWORK

Abstract

The Phishing is a cyber crime where a person who poses as a legitimate agency contacts a victim or target via email, phone or text message to attract the person to supply in-formation, information about personal identity, banking and credit card information and passwords. phishing is a crime. The new term ’fishing’ refers to the attacker’s invitation to visit a counterfeit site by creating a website look, and to get personal information from users such as username, password, financial information, account details, national security identifier, etc.. Phishing is a new term that was developed using ’fishing. The information collected is used for potential target ads or even identity robberies, attacks (for example, money transfer from one’s account). The attack method that is widely used is to send e-mails, messages that can lead to data theft or personal information. Social networking account Passwords, credit cards or attackers provide upgrades to their websites, encourage you to comply with your personal information and change it via fake website, are mis-entered daily. If you are entering your personal data, the attackers will collect it successfully on your server side, and will be able to carry out the next move with your information and to use it for their malicious purposes.

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"PHISHING WEBSITE DETECTION USING DEEP LEARNING FRAMEWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.h525-h530, May-2022, Available :http://www.jetir.org/papers/JETIR2205870.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"PHISHING WEBSITE DETECTION USING DEEP LEARNING FRAMEWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. pph525-h530, May-2022, Available at : http://www.jetir.org/papers/JETIR2205870.pdf

Publication Details

Published Paper ID: JETIR2205870
Registration ID: 402073
Published In: Volume 9 | Issue 5 | Year May-2022
DOI (Digital Object Identifier):
Page No: h525-h530
Country: Pune, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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