A Phishing Detection Approach for Empowering Cybersecurity with Explainable AI and SelectKBest

Phishing websites are a big cyber threat in today’s online world. They pretend to be real websites and steal personal information from users who are not aware of it. With more users accessing the internet quickly and knowing how to use computers, there’s been a rise in these fake sites that look very real and are made to trick people. It’s a challenging task to spot and sort out these fake sites because of their complexity and dissimilarity in nature. Our study looks at how well the SelectKBest feature selection method works to make tree-based classifiers like Random Forest (RF) and ExtraTrees (ETC) better at finding these phishing sites. We found that by picking the best 40 to 60 features, these classifiers could identify phishing sites with an accuracy of 96.68% after we fine-tuned their settings. We also used SHAP(SHapley Additive exPlanations) analysis, which helps us understand which parts of the data are most important, making our models more reliable and trustworthy. Our research is a step towards creating systems that can detect phishing sites in real-time and quickly warn people about them, which will help improve online security.