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Article . 2022
License: CC BY NC
Data sources: ZENODO
ZENODO
Article . 2022
License: CC BY NC
Data sources: Datacite
ZENODO
Article . 2022
License: CC BY NC
Data sources: Datacite
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Cyber Fraud Social Media Application

Authors: Lonari Prathmesh Ganesh; Gote Anisha Anand; Pandhare Jyotsana Satish; Divekar Apeksha Vijay; D. A. Gaikwad;

Cyber Fraud Social Media Application

Abstract

In the 20th century, most people are depends on the internet. So the chances of cyber fraud are increased rapidly. The purpose of these frauds is to get the information used by individuals and organizations for transactions and misuse of information. We proposed social media application for detecting phishing URLs or IP addresses based on Extreme Learning Machine Algorithm. Where people can communicate with each other to get more information about these frauds. We proposed an application based on machine learning techniques to detect phishing websites. It compares with 23 parameters and gives us one output based on these parameters. The output is in the form of normal, suspicious, and phishing.

Keywords

Machine Learning, Phishing, Extreme Learning Machine Algorithm

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    popularity
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    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.
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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!
0
Average
Average
Average
Green