CalMal is a project focused on detecting and classifying malware behavior using machine learning techniques. It assumes the availability of a dataset in JSON format within the "data/json" directory, which can be modified in the config.ini file.
- Python 3
- Docker (optional, for Docker-based setup)
- Git
- Clone the Repository
git clone git@github.com:unknownhad/CalMal.git
cd CalMalFollow the instructions at Python Poetry Documentation to install Poetry on your machine.
poetry shell
poetry install
poetry run python app.py
Access the web service by navigating to http://localhost:1234 in your browser. You can test predictions by uploading a JSON file.
For training : Put all the JSON from VirusTotal to /data/json then run
poetry run python data_process.py
This will process the data and make it consumeable
After that run :
poetry run python data_encoder.py
This will encode the baove data ot generate csv file.
Example output :
(calmal-py3.11) bash-3.2$ poetry run python data_encoder.py
Device used : cpu
Pytorch version: 2.2.1
Loading dataset from: /CalMal/result/temporary/dataset.csv.xz
0
Name: count, dtype: int64
Epochs [ 1/600], Batch [ 5/25], Loss = 0.04834136
Epochs [ 1/600], Batch [10/25], Loss = 0.03662824
Epochs [ 1/600], Batch [15/25], Loss = 0.03420896
Epochs [ 1/600], Batch [20/25], Loss = 0.02952765
......................Trimmed......................
......................Trimmed......................
After that run
poetry run python train.py
For training the model and finding the aquracy.
(calmal-py3.11) bash-3.2$ poetry run python train.py
Device used : cpu
Pytorch version: 2.2.1
Size of training dataset: 857
Size of testing dataset: 349
Previous checkpoint model found!
Final Accuracy = 0.0057306590257879654
Visualization Result:
Contributions to CalMal are welcome! Please follow the established coding and commit message guidelines. For more details, refer to the contribution guide in the repository.
For questions or contributions, please open an issue or a pull request in the GitHub repository.