Abstract:

Phishing attacks are a common form of social engineering used by attackers to steal sensitive information from users by tricking them into revealing their credentials, such as passwords and usernames. The traditional approach of blacklisting known phishing websites is not effective since attackers constantly create new phishing websites that are not on the blacklist. Machine learning algorithms can help to improve phishing detection by analyzing the URL features and classifying them as legitimate or phishing based on patterns learned from a data set. The Random Forest algorithm is a popular choice for this task due to its ability to handle high-dimensional data and provide accurate results. A Chrome browser extension or plugin can be a useful tool to integrate phishing detection into the user's browsing experience and provide real-time alerts when a suspicious link is clicked.

Description:

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