Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare
arxiv(2024)
摘要
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.
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