This repository contains the public version of the code for our work on cyberattack detection in SDN-based smart grids at line rate leveraging user-plane inference. The paper has been accepted for presentation at the ACM SIGEnergy Workshop on Cybersecurity and Privacy of Energy Systems (EnergySP'24), co-located with ACM e-Energy 2024, 4 - 7 June 2024, Singapore.
There are two folders:
- User_Plane_Inference : P4 code compiled and tested on an Intel Tofino switch, and the model table entries file.
- Data_Analysis : scripts and instructions for processing the data, the Jupyter notebooks for training the machine learning models, and the Python scripts for generating the M/A table entries from the saved trained models.
The use case considered in the paper is a DNP3 attack detection and classification use case based on the publicly available DNP3 Intrusion Detection Dataset.
The challenge is to classify traffic into one of 7 classes of which 1 is benign and 6 are malicious.
If you need additional information, please email us at aristide.akem at imdea.org.