Network and Device Level Cyber Deception for Contested Environments Using RL and LLMs

TL;DR

This paper reviews AI-driven network and device-level cyber deception strategies, emphasizing the integration of large language models and reinforcement learning to enhance the effectiveness and cost-efficiency of deception in contested environments.

Contribution

It introduces a comprehensive review of AI-based cyber deception methods, highlighting the fusion of LLMs and RL for dynamic and optimized deception strategies.

Findings

LLMs and RL can improve deception accuracy and adaptability.

AI-based deception strategies are effective against stealthy OT attacks.

Potential for cost reduction and automation in cyber deception.

Abstract

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.