Web URL Detection(Malicious/Safe) using Machine Learning

Steps for reproducing the project -

• Install all the required packages using the following command - pip install -r requirements.txt.
• Run the Flask App - python myapp.py
• Goto localhost:5000/train/ to train the model
• Goto localhost:5000/check/ to test the model, pass the form data url containing url.
• Done!

Description

This is a chrome extension with an additional website for real-time malicious web content detection. A binary classifier has been trained using Random Forest Classification algorithm to classify websites as malicious or benign. 22 features of the url have been used for the training of the model. The accuracy of the model's prediction is 96%.