The Road Towards Autonomous Cybersecurity Agents: Remedies for Simulation Environments

Warning

This publication doesn't include Faculty of Education. It includes Faculty of Informatics. Official publication website can be found on muni.cz.
Authors

DRAŠAR Martin RUMAN Ádám ČELEDA Pavel SCHANCHIEH Yang

Year of publication 2024
Type Article in Proceedings
Conference ESORICS 2023: Computer Security. ESORICS 2023 International Workshops
MU Faculty or unit

Faculty of Informatics

Citation
Doi http://dx.doi.org/10.1007/978-3-031-54129-2_43
Keywords simulation environments; autonomous decision-making; cybersecurity
Attached files
Description One of the fundamental challenges in developing autonomous cybersecurity agents (AICA) is providing them with appropriate training environments for skills acquisition and evaluation. Current reinforcement learning (RL) algorithms rely on myriads of training runs to instill proper behavior, and this is reasonably achievable only within a simulated environment. In this paper, we explore the topic of simulation models and environments for RL and present an assessment framework to compare simulation models designed for simulating cyberattack scenarios. We examine four existing simulation tools, including a new one by the authors of the paper, and discuss their properties, particularly in terms of deployability, to support RL-based AICA. In the example of complex scenarios, we compare the two most sophisticated simulation tools and discuss their strengths.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.