I Taught a Computer to Spot Fake Websites With 96% Accuracy

A developer built a machine learning model that detects phishing websites from URL and page features with 96.7% accuracy, without opening the sites. Trained on the UCI Phishing Websites dataset of 11,055 labeled sites across 30 features, a Random Forest classifier outperformed logistic regression (92.45%) and SVM (94.71%), with SSL certificate state and anchor link behavior emerging as the dominant predictive features. The code is published on GitHub.

What if a computer could tell a fake website is fake just by looking at its URL — without ever opening it? Turns out, yes. Here's how I built my first real machine learning project. If you've ever gotten a message saying "Your bank account is suspended, click here now," you've seen phishing in action — fake websites disguised as real ones, built to steal your password or credit card number. Traditional defenses blacklists of known bad URLs are always playing catch-up. Attackers spin up new fake domains faster than blacklists can be updated. Instead of memorizing bad URLs, what if a model learned the shared traits of phishing sites? Things like: I used the UCI Phishing Websites dataset — 11,055 real websites, each labeled phishing or legitimate, described by 30 features. I trained and compared three classic ML algorithms: | Algorithm | Accuracy | |---|---| | Logistic Regression | 92.45% | | Random Forest | 96.70% 🏆 | | SVM | 94.71% | When I checked which features mattered most, SSL certificate state and anchor link behavior dominated — by a wide margin over the other 28 features. Why? Because phishing sites usually: The model figured this out on its own — I never told it to focus on SSL. The biggest lesson: you don't need to be an expert to start. The dataset was ready-made, the tools Python + scikit-learn are free, and the steps are well-documented. What it actually took was patience and consistency. Full code and details are on GitHub: 🔗 https://github.com/eln2mac-has/phishing-detection-ml https://github.com/eln2mac-has/phishing-detection-ml If you try something similar or have questions, drop a comment below 👇