Phishing website test detection involves screening for attackers impersonating a brand to trick victims into revealing sensitive information. Attackers will usually create fake web pages which resemble the legitimate site they are targeting, with the aim of stealing login details and other data that can then be used for fraudulent activities. Typical indicators of phishing domains include the website’s URL, the web page content, and the hostname associated with the domain.

While there are several tools available for phishing domain detection, the most effective solution is to use a cyber threat intelligence platform. These platforms can identify phishing attacks and send alerts to appropriate parties to take action.

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The best phishing domain detection models are those that are simple to implement and use minimal resources. For example, using search engines for detection of phishing websites can be a good option because it is easy to deploy and does not require additional servers or complex software. Detection solutions should also avoid features that can be spoofed and that can lead to the creation of phishing sites.

For example, checking whether a website is running over HTTPS has been a feature used in phishing domain detection but it can be easily spoofed. It is also important to use a model that does not depend on one or two features and therefore should be evaluated for its overall accuracy. The research that was carried out in this article analyzed four machine learning models for phishing domain detection: artificial neural networks (ANNs), decision trees (DTs), support vector machines (SVMs) and random forests (RF). The results show that the RF model has the highest phishing detection accuracy rate at 97%.