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

TL;DR

The work addresses the rising complexity of cyber warfare by advocating a symbiotic integration of game-theoretic models (GMs) and foundation models (FMs) to enable proactive cyber deception. It develops a multi-level security-game framework (strategic, operational, tactical) and introduces FM-enabled GMs with neurosymbolic learning and meta-learning to adapt to nonstationary environments, including game-theoretic digital twins and mechanism design for security. The contributions include a taxonomy of hierarchical game interactions, a cross-level architecture for coordinating tactical, operational, and strategic decisions, and a neurosymbolic learning paradigm that conjectures attacker behavior and optimizes defense updates under contextual shifts. The approach promises decision dominance and adaptive guardware supporting descriptive, predictive, and prescriptive analytics, while acknowledging challenges such as data scarcity, real-time inference, and transfer learning between contexts that warrant further research and development.

Abstract

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.

Figures (7)

• Figure 1: Multi-level game-theoretic frameworks: strategic level, operational level, and tactical level games. Strategic level games are games that describe high-level decision-making, such as resource allocations and investment planning. The goal of strategic level games is to create long-term planning to achieve overarching objectives of the cyber warefare. Tactical-level games involve specific actions and maneuvers that can be implemented to achieve immediate objectives to support the overarching strategy. Examples of tactics in cyber warfare include the configuration of honeypots and the attacker engagement policies. The operational-level games sit between the strategic and tactical levels, focusing on the planning and coordination of a sequence of defense actions. Examples include the planning of a series of cyber defense strategies starting from intelligence gathering to counter lateral movement to achieve strategic level goals.
• Figure 2: An example of game modeling: An attacker aims to carry out a cyber kill chain to reach the target while a defender aims to deter and thwart this operation. The goal of the attacker is determined through strategic-level reasoning. It can be viewed as an outcome of a high-level game description. Once the goal is set, the cyber kill chain determines the tactics, techniques, and procedures (TTP) to achieve its goal, while the defender determines the defending TTPs. This operation is composed of a sequence of tactic-level games can provide specific techniques and actions. Each tactic level game corresponds to a stage in the operation. The games will yield tactics that determine the outcome at each stage and, eventually, the outcome of the operation. An adaptive operation is often used to reconfigure the operation when the operation fails at certain stages. In this case, the games will need to be redesigned and synthesized to adapt to the uncertainties in the outcomes.
• Figure 3: Game-theoretic models and FMs are fused together to create function modules for cyber defense, which will be built into the guardware. Game-theoretic models are representations of security scenarios while FMs are tailored for different tasks and applications. The guardware is composed of multiple function modules that are enabled different games and FMs. Each function module requires a different architecture to synthesize game and FMs.
• Figure 4: Examples of Architectures of Symbiotic FMs and GMs: (a) FM $F_1$ enriches GM $G_1$ by improving the precision of the GM and augmenting its learning capabilities; (b) Tactical games $G_1$ and $G_2$ are fused together to achieve an operation game through the FM; (c) The adaptation of two sequential tactic games $G_1$ and $G_2$ is enabled by FM $F_1$ and $G_2$. They are coordinated sequentially; (d) The operational level game learning $F_3$ coordinates the learning of $F_1$ and $F_2$.
• Figure 5: The encoder-decoder structure of the Transformer architecture, adapted from vaswani17transformer. Each input datapoint, after embedding and positional encoding, is fed to the attention module in the encoder part (the right half), which extracts temporal correlation (attention scores) across the input datapoint sequence. The attention scores are then passed to the decoder (the left half) to generate an output sequence auto-regressively, together with additional attention within the output sequence. The attention mechanism is instrumental in descriptive, predictive, and perspective analytics in cybersecurity.