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UNMASKING CYBER THREATS - LEVERAGING MACHINE LEARNING TO DETECT PHISHING WEBSITES.

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Název: UNMASKING CYBER THREATS - LEVERAGING MACHINE LEARNING TO DETECT PHISHING WEBSITES.
Autoři: S., Sherine, Kirubakaran, S. Stewart, Kala, I.
Zdroj: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 4, p488-502, 15p
Témata: SOCIAL engineering (Fraud), CYBERTERRORISM, STATISTICAL learning, MACHINE learning, PHISHING
Abstrakt: Nowadays, smart phones are widely used, which makes them sus- ceptible to phishing. The majority of phishing websites attempt to obtain the victim's data by using the same user interface and universal resource location (URL) as the legitimate websites (user name, password, credit card details, etc.). Protecting users from cyberattacks requires an intelligent strategy. Phish- ing can hurt a company in a number of ways, including loss of financial proper- ty, intellectual loss, reputation damage, and disturbance of business activities. As a result, there is a pressing need for a mobile phishing detection system. This project's primary objective is to predict phishing websites, which are common social engineering techniques that resemble trustworthy URLs and webpages. The important goal of this research is to identify websites that are vulnerable to user privacy. Algorithms for statistical machine learning are used for the detection. The algorithm is made to determine a website's quality based on a few characteristics (such as spam reports, report counts, etc.) and user ac- tivity on the website. In the actual world, it functions by notifying consumers as they browse a specific website. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Nowadays, smart phones are widely used, which makes them sus- ceptible to phishing. The majority of phishing websites attempt to obtain the victim's data by using the same user interface and universal resource location (URL) as the legitimate websites (user name, password, credit card details, etc.). Protecting users from cyberattacks requires an intelligent strategy. Phish- ing can hurt a company in a number of ways, including loss of financial proper- ty, intellectual loss, reputation damage, and disturbance of business activities. As a result, there is a pressing need for a mobile phishing detection system. This project's primary objective is to predict phishing websites, which are common social engineering techniques that resemble trustworthy URLs and webpages. The important goal of this research is to identify websites that are vulnerable to user privacy. Algorithms for statistical machine learning are used for the detection. The algorithm is made to determine a website's quality based on a few characteristics (such as spam reports, report counts, etc.) and user ac- tivity on the website. In the actual world, it functions by notifying consumers as they browse a specific website. [ABSTRACT FROM AUTHOR]
ISSN:11358599