An algorithm that takes malware samples as input and converts them into 256x256 bitmap images. Malware of similar families will generate similar looking images. This gives a useful initial visual representation of the malware and can be used to make initial triage easier.
The resulting images can further be used to train ANN models.
To get started:
- Clone the repo:
git clone https://github.com/edwinfredy/Malware-Classification-Using-Image-Processing-Techniques.git - Install dependencies:
pip install -r requirements.txt - Run the app:
streamlit run main.py
Image generated from Andromeda Trojan
SHA-3529fdeb51006fd75fa7d19d9b686d64de7b2b89f7eb90b5f9e0e83b82693b28
Image generated from Guloader Malware
SHA-e9aecc07a864c95c949393e7603cfda409a643d86f8cc6da0c1011fdff62f7e0
Image generated from Clipbanker Trojan
SHA-3241590d83e64c4274595c8d96c9db08df8db169cc54ecde703184ad9da7dc5a
