Network and Device Level Cyber Deception for Contested Environments Using RL and LLMs
This work addresses the problem of improving accuracy and cost-effectiveness in cyber deception for contested environments, but it is incremental as it reviews and builds upon existing AI methods.
The paper reviews AI-based solutions for network- and device-level cyber deception in contested environments, focusing on leveraging large language models and reinforcement learning to learn and validate deception strategies against stealthy attacks on operational technology systems.
Cyber deception assists in increasing the attacker's budget in reconnaissance or any early phases of threat intrusions. In the past, numerous methods of cyber deception have been adopted, such as IP address randomization, the creation of honeypots and honeynets mimicking an actual set of services, and networks deployed within an enterprise or operational technology(OT) network. These types of strategies follow naive approaches of recreating services that are expensive and that need a lot of human intervention. The advent of cloud services and other automations of containerized applications, such as Kubernetes, makes cyber defense easier. Yet, there remains a lot of potential to improve the accuracy of these deception strategies and to make them cost-effective using artificial intelligence (AI)-based solutions by making the deception more dynamic. Hence, in this work, we review various AI-based solutions in building network- and device-level cyber deception methods in contested environments. Specifically, we focus on leveraging the fusion of large language models (LLMs) and reinforcement learning(RL) in optimally learning these cyber deception strategies and validating the efficacy of such strategies in some stealthy attacks against OT systems in the literature.