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

arXiv: https://arxiv.org/abs/2403.10570 · HTML: https://mlsanity.com/paper/2403.10570/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 114 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 30 of 41)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://mlsanity.com/paper/2106.02039/) (2021)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021)
- [The Confluence of Networks, Games and Learning](https://mlsanity.com/paper/2105.08158/) (2021)
- [A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy](https://mlsanity.com/paper/1712.05441/) (2017)
- [A Dynamic Games Approach to Proactive Defense Strategies against Advanced Persistent Threats in Cyber-Physical Systems](https://mlsanity.com/paper/1906.09687/) (2019)
- [Symbol-LLM: Leverage Language Models for Symbolic System in Visual Human Activity Reasoning](https://mlsanity.com/paper/2311.17365/) (2023)
- [Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games](https://mlsanity.com/paper/2402.18781/) (2024)
- [Blackwell Online Learning for Markov Decision Processes](https://mlsanity.com/paper/2012.14043/) (2020)
- [Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation](https://mlsanity.com/paper/2210.03209/) (2022)
- [Scenario-Agnostic Zero-Trust Defense with Explainable Threshold Policy: A Meta-Learning Approach](https://mlsanity.com/paper/2303.03349/) (2023)
- [Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains](https://mlsanity.com/paper/2006.02579/) (2020)
- [Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learning](https://mlsanity.com/paper/2306.06766/) (2023)
- [Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes](https://mlsanity.com/paper/1906.12182/) (2019)
- [On the Price of Transparency: A Comparison between Overt Persuasion and Covert Signaling](https://mlsanity.com/paper/2304.00096/) (2023)
- [Berk-Nash Equilibrium: A Framework for Modeling Agents with Misspecified Models](https://mlsanity.com/paper/1411.1152/) (2014)
- [Duplicity Games for Deception Design with an Application to Insider Threat Mitigation](https://mlsanity.com/paper/2006.07942/) (2020)
- [Continuous-Time Markov Decision Processes with Controlled Observations](https://mlsanity.com/paper/1907.06128/) (2019)
- [A First Order Meta Stackelberg Method for Robust Federated Learning](https://mlsanity.com/paper/2306.13800/) (2023)
- [Dynamic Bayesian Games for Adversarial and Defensive Cyber Deception](https://mlsanity.com/paper/1809.02013/) (2018)
- [ADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Prevention](https://mlsanity.com/paper/2106.06907/) (2021)
- [On the Resilience of Traffic Networks under Non-Equilibrium Learning](https://mlsanity.com/paper/2210.03214/) (2022)
- [Decision-Dominant Strategic Defense Against Lateral Movement for 5G Zero-Trust Multi-Domain Networks](https://mlsanity.com/paper/2310.01675/) (2023)
- [Modeling and Analysis of Leaky Deception using Signaling Games with Evidence](https://mlsanity.com/paper/1804.06831/) (2018)
- [A Pursuit-Evasion Differential Game with Strategic Information Acquisition](https://mlsanity.com/paper/2102.05469/) (2021)
- [Is Stochastic Mirror Descent Vulnerable to Adversarial Delay Attacks? A Traffic Assignment Resilience Study](https://mlsanity.com/paper/2304.01161/) (2023)

## Built on by (most cited first, 8 of 8)

- [Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization](https://mlsanity.com/paper/2508.14385/) (2025)
- [From Texts to Shields: Convergence of Large Language Models and Cybersecurity](https://mlsanity.com/paper/2505.00841/) (2025)
- [Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundations](https://mlsanity.com/paper/2512.22883/) (2025)
- [In-Context Autonomous Network Incident Response: An End-to-End Large Language Model Agent Approach](https://mlsanity.com/paper/2602.13156/) (2026)
- [Learning from Response not Preference: A Stackelberg Approach for LLM Detoxification using Non-parallel Data](https://mlsanity.com/paper/2410.20298/) (2024)
- [Agentic Incident Response through Digital Twin-Enhanced Multiscale Planning](https://mlsanity.com/paper/2608.02422/) (2026)
- [The Game-Theoretic Symbiosis of Trust and AI in Networked Systems](https://mlsanity.com/paper/2411.12859/) (2024)
- [Guarding Against Malicious Biased Threats (GAMBiT) Experiments: Revealing Cognitive Bias in Human-Subjects Red-Team Cyber Range Operations](https://mlsanity.com/paper/2508.20963/) (2025)

## Abstract (arXiv)

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.