A brief paper on this repo is available as Johnson & Ekstedt, "Towards a Graph Neural Network-Based Approach for Estimating Hidden States in Cyber Attack Simulations", 2023. Another brief paper, on the concept of detectors is available as Johnson, Ekstedt & Kakouros, "Introducing_Threat_Detectors_in_the_Meta_Attack_Language.pdf", 2024.
The code can run in GitHub Codespaces, as well as in GCP Batch and GCP Vertex AI.
In Codespaces (or some other execution environment), try python3 main train.
To run it in GCP Vertex AI for hyperparameter tuning, use hp_tuning.sh.
To run it in GCP Batch for parallel simulation, use batch.sh.
You will need a GCP bucket, and you will need to provide a GCP service account with access to that bucket.
The project is also integrated with Weights & Biases, so you will need a wandb API key.