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Detecting Phishing Websites using Data Mining

Authors: Mehek Thaker; Mihir Parikh; Preetika Shetty; Vinit Neogi; Shree Jaswal;

Detecting Phishing Websites using Data Mining

Abstract

Phishing is one of the major cyber threats now, where the victims' credentials are obtained by an illegitimate website. This paper proposes a system which will detect old as well as newly generated phishing URLs that have completely no past behaviours to judge upon, using Data Mining. A cloud-based classification model will be created for the same wherein various extracted attributes through the URL will be used as input data. The model will be trained with an exhaustive dataset so as to ensure maximum accuracy.

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Top 10%
Top 10%
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