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- Title
Phish-Sight: a new approach for phishing detection using dominant colors on web pages and machine learning.
- Authors
Pandey, Pankaj; Mishra, Nishchol
- Abstract
Phishing is one of the most dangerous threats in which a hacker imitates a person, company or government agency to lure and deceive their victims. Machine learning anti-phishing solutions are gaining popularity nowadays. However, most anti-phishing solutions rely heavily on features extracted from third-party services such as whois services, DNS search, and web traffic. As a result, they are slow and require a lot of computing resources. This paper introduces a machine-learning-based framework: Phish-Sight that detects phishing websites through a visual inspection strategy. Phish-Sight uses dominant color features and highly targeted popular brand names embedded in URLs' web pages with machine learning techniques to detect phishing web pages. Prediction performance of the dominant color features and popular brand names from web pages was investigated using five machine learning algorithms. The Random Forest algorithm surpassed the others, with a 98.43% true positive rate and 99.13% accuracy in detecting phishing frauds. The prediction run time per web page measured at 7.6 s suggests that Phish-Sight has potential for real-time applications.
- Subjects
WEBSITES; MACHINE learning; PHISHING; INTERNET traffic; UNIFORM Resource Locators; RANDOM forest algorithms; PHISHING prevention
- Publication
International Journal of Information Security, 2023, Vol 22, Issue 4, p881
- ISSN
1615-5262
- Publication type
Article
- DOI
10.1007/s10207-023-00672-4