Autonomous Cyber Operations Gym for Training and Evaluating AI Models for Defence
This paper proposes an Autonomous Cyber Operations (ACO) gym, a unified framework integrating high-fidelity network emulation, realistic user behaviour modelling for benign network traffic and adversarial profiles, and the generation of cybersecurity scenarios through open toolkits. By supporting live and deferred feature extraction at the packet and flow levels, the gym facilitates the creation of labelled synthetic datasets for training Deep Neural Network (DNN)-based Intrusion Detection Systems (IDSs), as well as developing and evaluating modular components for autonomous defence agents through emulated interactions. While evaluation of a DNN classifier within the gym shows high in-dom...
| Authors | Tobias Hürten, Johannes F. Loevenich, Florian Spelter et al. |
|---|---|
| Primary category | cs.CR |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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