# Decision-Dominant Strategic Defense Against Lateral Movement for 5G Zero-Trust Multi-Domain Networks

arXiv: https://arxiv.org/abs/2310.01675 · HTML: https://mlsanity.com/paper/2310.01675/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2023.

## Built on by (most cited first, 6 of 6)

- [Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games](https://mlsanity.com/paper/2402.18781/) (2024)
- [Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare](https://mlsanity.com/paper/2403.10570/) (2024)
- [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)
- [Agentic Incident Response through Digital Twin-Enhanced Multiscale Planning](https://mlsanity.com/paper/2608.02422/) (2026)
- [Hierarchical Agentic Incident Response with Digital-Twin-Validated Attack Inference](https://mlsanity.com/paper/2608.15016/) (2026)

## Abstract (arXiv)

Multi-domain warfare is a military doctrine that leverages capabilities from different domains, including air, land, sea, space, and cyberspace, to create a highly interconnected battle network that is difficult for adversaries to disrupt or defeat. However, the adoption of 5G technologies on battlefields presents new vulnerabilities due to the complexity of interconnections and the diversity of software, hardware, and devices from different supply chains. Therefore, establishing a zero-trust architecture for 5G-enabled networks is crucial for continuous monitoring and fast data analytics to protect against targeted attacks. To address these challenges, we propose a proactive end-to-end security scheme that utilizes a 5G satellite-guided air-ground network. Our approach incorporates a decision-dominant learning-based method that can thwart the lateral movement of adversaries targeting critical assets on the battlefield before they can conduct reconnaissance or gain necessary access or credentials. We demonstrate the effectiveness of our game-theoretic design, which uses a meta-learning framework to enable zero-trust monitoring and decision-dominant defense against attackers in emerging multi-domain battlefield networks.