Abstract
In recent years, phishing has become one of the most widespread and dangerous cyber threats. These attacks aim to obtain users' confidential information, such as passwords and credit card details, through deceptive messages or websites, making the issue of protection against them more relevant than ever. Traditional methods of phishing protection, such as blacklists and heuristic analysis, can no longer keep up with the evolving pace of phishing attacks. Therefore, there is a need to develop more advanced and intelligent methods, among which machine learning (ML) techniques play a significant role. This article discusses various ML methods used for automatic detection of phishing URLs. The study presents the main approaches, model architectures, advantages and disadvantages of each method, and provides a comparative analysis of their effectiveness on real data.
Published Version
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