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Datasets for phishing websites detection.

ABSTRACT: Phishing stands for a fraudulent process, where an attacker tries to obtain sensitive information from the victim. Usually, these kinds of attacks are done via emails, text messages, or websites. Phishing websites, which are nowadays in a considerable rise, have the same look as legitimate sites. However, their backend is designed to collect sensitive information that is inputted by the victim. Discovering and detecting phishing websites has recently also gained the machine learning community's attention, which has built the models and performed classifications of phishing websites. This paper presents two dataset variations that consist of 58,645 and 88,647 websites labeled as legitimate or phishing and allow the researchers to train their classification models, build phishing detection systems, and mining association rules.

SUBMITTER: Vrbancic G

PROVIDER: S-EPMC7642806 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Datasets for phishing websites detection.

Vrbančič Grega G Fister Iztok I Podgorelec Vili V

Data in brief 20201023

Phishing stands for a fraudulent process, where an attacker tries to obtain sensitive information from the victim. Usually, these kinds of attacks are done via emails, text messages, or websites. Phishing websites, which are nowadays in a considerable rise, have the same look as legitimate sites. However, their backend is designed to collect sensitive information that is inputted by the victim. Discovering and detecting phishing websites has recently also gained the machine learning community's ...[more]

PMID: 33195768

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Project description:BackgroundThe onset of the COVID-19 pandemic and the concurrent development of vaccines offered a rare and somewhat unprecedented opportunity to study antivaccination behavior as it formed over time via the use of archived versions of websites.ObjectiveThis study aims to assess how existing antivaccination websites modified their content to address COVID-19 vaccines and pandemic restrictions.MethodsUsing a preexisting collection of 25 antivaccination websites curated by the IvyPlus Web Collection Program prior to the pandemic and crawled every 6 months via Archive-It, we conducted a content analysis to see how these websites acknowledged or ignored COVID-19 vaccines and pandemic restrictions. Websites were assessed for financial behaviors such as having storefronts, mention of COVID-19 vaccines in general or by manufacturer name, references to personal freedom such as masking, safety concerns like side effects, and skepticism of science.ResultsThe majority of websites addressed COVID-19 vaccines in a negative fashion, with more websites making appeals to personal freedom or expressing skepticism of science than questioning safety. This can potentially be attributed to the lack of available safety data about the vaccines at the time of data collection. Many of the antivaccination websites we evaluated actively sought donations and had a membership option, evidencing these websites have financial motivations and actively build a community around these issues. The content analysis also offered the opportunity to test the viability of archived websites for use in scholarly research. The archived versions of the websites had significant shortcomings, particularly in search functionality, and required supplementation with the live websites. For web archiving to be a viable source of stand-alone content for research, the technology needs to make significant improvements in its capture abilities.ConclusionsIn summary, we found antivaccination websites existing prior to the COVID-19 pandemic largely adapted their messaging to address COVID-19 vaccines with very few sites ignoring the pandemic altogether. This study also demonstrated the timely and significant need for more robust web archiving capabilities as web-based environments become more ephemeral and unstable.

Project description:BACKGROUND:Hospitals have been one of the major targets for phishing attacks. Despite efforts to improve information security compliance, hospitals still significantly suffer from such attacks, impacting the quality of care and the safety of patients. OBJECTIVE:This study aimed to investigate why hospital employees decide to click on phishing emails by analyzing actual clicking data. METHODS:We first gauged the factors that influence clicking behavior using the theory of planned behavior (TPB) and integrating trust theories. We then conducted a survey in hospitals and used structural equation modeling to investigate the components of compliance intention. We matched employees' survey results with their actual clicking data from phishing campaigns. RESULTS:Our analysis (N=397) reveals that TPB factors (attitude, subjective norms, and perceived behavioral control), as well as collective felt trust and trust in information security technology, are positively related to compliance intention. However, compliance intention is not significantly related to compliance behavior. Only the level of employees' workload is positively associated with the likelihood of employees clicking on a phishing link. CONCLUSIONS:This is one of the few studies in information security and decision making that observed compliance behavior by analyzing clicking data rather than using self-reported data. We show that, in the context of phishing emails, intention and compliance might not be as strongly linked as previously assumed; hence, hospitals must remain vigilant with vulnerabilities that cannot be easily managed. Importantly, given the significant association between workload and noncompliance behavior (ie, clicking on phishing links), hospitals should better manage employees' workload to increase information security. Our findings can help health care organizations augment employees' compliance with their cybersecurity policies and reduce the likelihood of clicking on phishing links.

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Datasets for phishing websites detection.

Publications

Datasets for phishing websites detection.

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