Paper ID: 2403.10570

Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare

Tao Li, Quanyan Zhu

We are currently facing unprecedented cyber warfare with the rapid evolution of tactics, increasing asymmetry of intelligence, and the growing accessibility of hacking tools. In this landscape, cyber deception emerges as a critical component of our defense strategy against increasingly sophisticated attacks. This chapter aims to highlight the pivotal role of game-theoretic models and foundation models (FMs) in analyzing, designing, and implementing cyber deception tactics. Game models (GMs) serve as a foundational framework for modeling diverse adversarial interactions, allowing us to encapsulate both adversarial knowledge and domain-specific insights. Meanwhile, FMs serve as the building blocks for creating tailored machine learning models suited to given applications. By leveraging the synergy between GMs and FMs, we can advance proactive and automated cyber defense mechanisms by not only securing our networks against attacks but also enhancing their resilience against well-planned operations. This chapter discusses the games at the tactical, operational, and strategic levels of warfare, delves into the symbiotic relationship between these methodologies, and explores relevant applications where such a framework can make a substantial impact in cybersecurity. The chapter discusses the promising direction of the multi-agent neurosymbolic conjectural learning (MANSCOL), which allows the defender to predict adversarial behaviors, design adaptive defensive deception tactics, and synthesize knowledge for the operational level synthesis and adaptation. FMs serve as pivotal tools across various functions for MANSCOL, including reinforcement learning, knowledge assimilation, formation of conjectures, and contextual representation. This chapter concludes with a discussion of the challenges associated with FMs and their application in the domain of cybersecurity.

Submitted: Mar 14, 2024