DDoS-detect: Resource-Efficient DDoS Detection in IoT

This is a repository that demonstrates a proof of concept paper "Towards Resource-Efficient DDoS Detection in IoT: Leveraging Feature Engineering of System and Network Usage Metrics."

To build your own dataset, do the following:

1. Use Data/db_collector.py to build a dataset, as

python3 Data/db_collector.py

2. Manually label the dataset upon building it by adding a value to the .csv file called 'Attack-type' and name it 'labeled_db.csv'

3. Make sure the .csv file is in the /Data directory and run ml_train.py

python3 Deployment/ml_train.py

4. Run classifier.py to classify current device state. In case of porting to another device, make sure to include the .joblib files in the same directory.

python3 Deployment/classifier.py

If you wish to use this framework in your paper, please cite our paper.