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Abstract

Phishing website attacks are a type of cyber-attack in which perpetrators create fraudulent websites that mimic legitimate platforms, such as online banking or social media, with the intent of tricking unsuspecting users into divulging sensitive information. This includes passwords, credit card details, usernames, and other personal data. These phishing websites aredesigned to look authentic and often employ various techniques, such as URL spoofing, social engineering, and email or text message phishing, to lure victims into revealing their confidential information. Web apps are growing increasingly complex and difficult to identify at first glance, especially when they use encryption and obfuscation techniques. In order to effectively detect and stop phishing web applications from being uploaded to the server in real-time, machine learning must be developed. In addition to including analyses for the machine learning algorithms for identifying web application-based assaults, the study calibrates fresh analyses by executing machine learning algorithms and confirming the findings. The study uses unique and categorized results from a machine learning dataset. As per the outcomes obtained from experimental and comparative analyses of the applied classification algorithms, the random forest model demonstrated the highest accuracy, achieving an impressive rate of 96.89%,followedby the decision tree model at 94.57%,andExtreme Gradient Boosting (XG).

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