Build a Phishing Website Detection System Using Machine Learning

Phishing attacks are a prevalent threat in cybersecurity, tricking users into revealing sensitive information through deceptive websites. Traditional blacklists can't keep up with the rapid creation of new phishing sites. A machine learning-based detection system can analyze patterns and features to identify malicious websites in real-time, providing a proactive defense mechanism.

The system analyzes URLs and website content to extract features indicative of phishing. It employs machine learning models trained on datasets of legitimate and phishing websites to classify and detect threats. Users can input URLs to receive immediate risk assessments.

The system processes input URLs by extracting relevant features and feeding them into a trained machine learning model. Based on the analysis, it classifies the URL as legitimate or phishing. This approach enables the detection of new and evolving phishing threats that traditional methods might miss.

• Users input a URL into the system.
• The system extracts features from the URL and associated website content.
• Extracted features are processed by the machine learning model.
• The model outputs a classification: 'Legitimate' or 'Phishing'.
• Users receive the result with an explanation of the risk factors identified.

• Phishing Dataset for Machine Learning
• Scikit-learn Documentation
• Flask Web Framework
• React.js Official Website
• Understanding Phishing Attacks