Phishing Website Detection using Machine Learning
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Description:
Phishing website detection is a security measure that detects fake websites attempting to steal sensitive user data (e.g., passwords, credit card info). ML can analyze features such as URL patterns, page content, and domain reputation to identify phishing attempts.
Features:
- Detect phishing websites based on various features (e.g., URL structure, HTTPS usage, domain reputation).
- Train models on labeled datasets (e.g., Phishing Websites dataset).
- Provide a confidence score on whether a website is phishing.
Requirements:
- Programming Language: Python
- Libraries/Tools:
- Scikit-learn for machine learning models.
- Pandas for data preprocessing.
- Flask or Django (if building a web app to interact with the model).
Instructions:
- Collect a labeled dataset of phishing and legitimate websites.
- Preprocess the data (extract features from URLs, domain name, HTTP header).
- Train a classifier (Logistic Regression, Random Forest, SVM) on the features.
- Evaluate the model on test data and make predictions.
License:
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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