Malware-Detection-ML-Model

Demo: Youtube

Try at: Streamlit

• This is a Python program to train Malware Detection ML Model and check if a given file is a probable MALWARE or not!
• It uses Random Forest algorithm for training the ML model.
• I have implemented it in 2 ways:
  • CLI app
  • Streamlit app

NOTE: Don't run any files inside malwares folder, as these are actual malwares taken from GitHub.

Requirements (installable via pip)

• For running CLI app:
  • joblib
  • pefile
• For running Streamlit app:
  • streamlit
  • joblib
  • pefile
• For training your own model:
  • numpy
  • pandas
  • scikit-learn
  • joblib
  • pefile

What I used?

1. Scikit-learn - Scikit-learn (formerly scikits.learn and also known as sklearn) is a free software machine learning library for the Python programming language.
   • RandomForestClassifier
   • ExtraTreesClassifier
2. Malware Dataset - The raw data here was obtained from the malware security partner of Meraz'18 - Annual Techno Cultural festival of IIT Bhilai, the said raw data constituted malware and legitimate files.
3. Streamlit - for GUI - Streamlit is an open-source app framework for Machine Learning and Data Science teams.
4. Flask - for distributed system - Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions.

How to run the program?

1. Download this GitHub repository

   • Either Clone the repository

      git clone https://github.com/Kunal-Attri/Malware-Detection-ML-Model.git
     

   • Or download and extract the zip archive of the repository.

2. Download & Install requirements

   • Ensure that you have Python 3 installed.
   • Open terminal in the Repository folder on your local machine.
   • Run the following command to install requirements.

      pip3 install -r requirements.txt
     

3. Run CLI app

   • Get a file to check for probable malware, and run:

     python3 main.py [FILENAME]
     

   • Expected Interface
     
     
     

4. Run Streamlit app

   • Try on web app here.
   • or run locally by:

     streamlit run streamlit_app.py
     

   • Expected Interface
     
     
     

References

• Random Forest Documentation
• About Random Forest Algorithm
• Extra Trees Classifier
• Streamlit