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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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Phishing Website Dataset

Authors: Putra, I Kadek Agus Ariesta;

Phishing Website Dataset

Abstract

This dataset contains a collection of legitimate and phishing websites, along with information on the target brands (brands.csv) being impersonated in the phishing attacks. The dataset includes a total of 10,395 websites, 5,244 of which are legitimate and 5,151 of which are phishing websites. These websites impersonate a total of 86 different target brands. For phishing datasets, the files can be downloaded in a zip file with a "phishing" prefix, while for legitimate websites, the files can be downloaded in a zip file with a "not-phishing" prefix. In addition, the dataset includes features such as screenshots, text, CSS, and HTML structure for each website, as well as domain information (WHOIS data), IP information, and SSL information. Each website is labeled as either legitimate or phishing and includes additional metadata such as the date it was discovered, the target brand being impersonated, and any other relevant information. The dataset has been curated for research purposes and can be used to analyze the effectiveness of phishing attacks, develop and evaluate anti-phishing solutions, and identify trends and patterns in phishing attacks. It is hoped that this dataset will contribute to the advancement of research in the field of cybersecurity and help improve our understanding of phishing attacks.

Keywords

phishing

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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!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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168