https://niklas-lange.netlify.app/categories/security/Recent content in Security on Niklas LangeHugoen-gbTue, 31 Mar 2026 00:00:00 +0000https://niklas-lange.netlify.app/dissertation/Tue, 31 Mar 2026 00:00:00 +0000https://niklas-lange.netlify.app/dissertation/<h2 id="abstract">Abstract</h2> <p>Since the start of the internet, phishing has been a persistent problem, with attempts becoming more sophisticated and common in recent years. Prevention models are therefore needed to classify sites correctly as either phishing or legitimate at scale. This paper builds and compares three ML models — Random Forest, Logistic Regression and K-Nearest Neighbours — and a CNN model, using URL, metadata and HTML features collected from phishing and legitimate websites. The models were evaluated on both historical and modern datasets, with a key finding being that all models experienced significant accuracy degradation on modern data, with ML models dropping by ~30% and the CNN by ~48%, despite performing best on historical data.</p>