By RecOsint | Dec 6, 2025
Humans Are Too Slow. You receive 100 emails a day. You can't check every link manually. – The Goal: Build a Machine Learning model that looks at a URL and instantly says: Safe or Phishing. – The Tool: We will use Python and a library called Scikit-Learn.
AI needs examples to learn. We need a dataset containing thousands of: – Legit URLs: (https://www.google.com/search?q=google.com, amazon.com) – Phishing URLs: (secure-login-bank.xyz) – Source: Download a free dataset from Kaggle or PhishTank.
The computer can't read text like us. We must convert the URL into numbers (Features). Key things the AI looks for: – Length: Is the URL suspiciously long? (>54 chars) – Special Chars: Does it have too many @ or - symbols? – "https": Is the token missing?
– Phishing: paypal-secure-login-update.com (4 hyphens, 1 "login" keyword). – Legit: paypal.com (0 hyphens). The AI learns this pattern: More Hyphens + "Login" keyword = High Danger.
We feed these numbers into an algorithm like Random Forest. – The Code Logic: model.fit(features, labels) (Translation: "Hey Computer, look at these patterns and learn the difference.")
Now, give it a URL it has never seen before. – Input: netflix-payment-verify.net – AI Prediction: PHISHING (98% Confidence) – Success: You just built a cyber defense tool.
This is just the beginning. Real-world tools also check: – Domain Age: (Created yesterday? Suspicious). – Hosting Country: (Hosted in a high-risk region?).