TY - CHAP
T1 - Decision-Dominant Strategic Defense Against Lateral Movement for 5G Zero-Trust Multi-Domain Networks
AU - Li, Tao
AU - Pan, Yunian
AU - Zhu, Quanyan
PY - 2024
Y1 - 2024
N2 - 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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
AB - 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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85200518084&origin=recordpage
U2 - 10.1007/978-3-031-53510-9_2
DO - 10.1007/978-3-031-53510-9_2
M3 - RGC 12 - Chapter in an edited book (Author)
SN - 978-3-031-53509-3
T3 - Advances in Information Security
SP - 25
EP - 76
BT - Network Security Empowered by Artificial Intelligence
A2 - Chen, Yingying
A2 - Wu, Jie
A2 - Yu, Paul
A2 - Wang, Xiaogang
PB - Springer
CY - Cham
ER -