SMART DETECTION OF PHISHING WEBSITES WITH RANDOM FOREST MACHINE LEARNING
Abstract
Phishing is one of the most widely executed cybercrimes in the modern digital sphere
where an attacker imitates an existing - and often trusted - person or entity in an attempt
to capture a victim‟s login credentials, account information, and other sensitive data.
Phishing websites are visually and semantically similar to real ones. The rise in online
trading activities has resulted in a rise in the number of phishing scams. In this paper,
we utilised an Ensemble Machine Learning Methods such as Random Forest Algorithm
for efficient and accurate phishing website detection based on its Uniform Resource
Locator. A total of 90,000 URLs were considered for this study. 49805 of them which
are phishing URLs and 48902 of them which are legitimate URLs. The Random Forest
classifier produced an accuracy of 80.2%.
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