UGC Approved Journal no 63975(19)

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

Volume 5 Issue 3
March-2018
eISSN: 2349-5162

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

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Published Paper ID:
JETIR1803118


Registration ID:
180707

Page Number

659-663

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Title

Four Factor Authentication System Text Based Graphical Password Scheme Using Machine Learning

Abstract

Captcha is a security mechanism designed to differ-entiate between computers and humans, and is used to defend against malicious bot programs. Text-based Captchas are the most widely deployed differentiation mechanism, and almost all text-based Captchas are single-layered. Numerous successful at-tacks on the single-layer text-based Captchas deployed by Google, Yahoo! and Amazon have been reported. In new two-layer Captcha scheme was deployed in 2015 by microsoft. This appears to be the first application of two-layer Captchas. It is therefore natural to ask a fundamental question: is the two-layer Captcha as secure as its designers expected? Intrigued by this question, we have for the first time systematically analyzed the security of the two-layer Captcha in this paper.

Key Words

Four Factor Authentication System Text Based Graphical Password Scheme Using Machine Learning

Cite This Article

"Four Factor Authentication System Text Based Graphical Password Scheme Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 3, page no.659-663, March-2018, Available :http://www.jetir.org/papers/JETIR1803118.pdf

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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

"Four Factor Authentication System Text Based Graphical Password Scheme Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 3, page no. pp659-663, March-2018, Available at : http://www.jetir.org/papers/JETIR1803118.pdf

Publication Details

Published Paper ID: JETIR1803118
Registration ID: 180707
Published In: Volume 5 | Issue 3 | Year March-2018
DOI (Digital Object Identifier):
Page No: 659-663
Country: vellore, Tamil Nadu, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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