Data for the Paper "Scalable and Generalizable RL Agents for Attack Path Discovery via Continuous Invariant Spaces"

This repository contains the data used in the paper "Scalable and Generalizable RL Agents for Attack Path Discovery via Continuous Invariant Spaces".This data have been generated using the C-CyberBattleSim framework, an extension of the original Microsoft CyberBattleSim framework.The data includes the data scraped for generating scenarios, the scenarios generated, the training/testing/hyper-optimization results of the GAE, agent models, and the multi-label classifier used to label the vulnerabilities. The data is organized in the following way:- environment_database/: Contains the vulnerabilities scraped from NVD and Shodan regarding services and vulnerabilities used in the simulations.- scenarios/: Contains the scenarios generated using the environment database.- classifiers_data/: Contains the labeled vulnerabilities used for training the multi-label classifier.- gae_hyperopt/: Contains the results of the hyperparameter optimization for the GAE model.- gae_training/: Contains the results of the training & testing of the GAE model.- classifiers_hyperopt/: Contains the results of the hyperparameter optimization for the multi-label classifier model.- classifiers_training_testing/: Contains the results of the training & testing of the multi-label classifier model.- agent_hyperopt/: Contains the results of the hyperparameter optimization for the agent models.- agents_training_testing/: Contains the results of the training & testing of the agent models.- config/: The configuration files used in the study, divided in sub-folders according to the specific sub-study where used. The subfolders within each folder have been renamed to be self-explanatory. For any questions regarding reproducibility, please feel free to contact us. The C-CyberBattleSim tool includes a README file with detailed commands for effectively using this data.