A Crow Search Algorithm for Building a Fuzzy Classifier of Phishing Attacks

Phishing attacks are among the most common cyberattacks aimed at stealing user personal information. Machine learning methods have successfully been used to detect phishing attacks. One such method is fuzzy systems. The purpose of this article is to address the problem of spam detection using fuzzy classifiers optimized by the crow search algorithm. The efficiency of the constructed classifiers was tested with four data sets containing descriptions of phishing attacks. The obtained results were compared with the results of other classifiers. With comparable accuracy, fuzzy classifiers use fewer features.