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iCyberGuard: A FlipIt Game for Enhanced Cybersecurity in IIoT
Abstract
Social manufacturing has significantly advanced the industrial Internet of Things (IIoT), integrating information technology and operation technology to enhance production efficiency and quality, and to foster new business models. This integration, however, introduces novel risks, including advanced persistent threats, which demand robust security measures to safeguard IIoT systems. This article proposes an iCyberGuard game model, tailored for IIoT environments, designed to imitate the cyber and physical attacks for information and operation technologies. Then, we used a reinforcement learning algorithm to compute the optimal strategy. We conducted comprehensive simulation experiments, which demonstrate that our model the strategic interactions between attackers and defenders. Participants are enabled to learn adaptively, discerning optimal strategies based on the intelligence of their adversaries. Finally, we explain the practical significance of the best strategy of defenders or attackers, and how users can rely on these best strategies to strengthen the security performance of the network.
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In contrast to other data types that mainly target offline detection accuracy, simulation data are primarily used to learn adaptive defense policies (e.g., dynamic threshold control, attack-defense strategy selection, policy-based mitigation) in MDP or game-theoretic environments.
“First, in contrast to other data types that mainly target offline detection accuracy, simulation data are primarily used to learn adaptive defense policies such as dynamic threshold control, attack-defense strategy selection, and policy-based mitigation in MDP or game-theoretic environments [119-121].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [119]
The reviewed simulation-data studies show two representative ways of using simulation: cyber-range environments (e.g., CyberBattleSim) where the entire attack-defense process is simulated, and Markov game settings that reuse real ICS/CPS datasets while generating perturbed trajectories to evaluate robustness against unseen attacks.
“Second, the reviewed studies show two representative ways of using simulation: cyber-range environments such as CyberBattleSim, where the entire attack-defense process is simulated [119], and Markov game settings that reuse real ICS/CPS datasets while generating perturbed trajectories to evaluate robustness against unseen attacks [120,121].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [119]
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Metadata
- Authors
- Chen, X., Cao, W., Chen, L., Han, J., Yang, M., Wang, Z., & Wang, F.-Y.
- Affiliations
- Macau University of Science and Technology (MO)
- Hangzhou Dianzi University (CN)
- Xi'an Jiaotong University (CN)
- Venue
- IEEE Transactions on Computational Social Systems
- Published
- 2024-12
- Volume
- 11
- Issue
- 6
- Pages
- 8005-8014
- ISSN
- 2329-924X (ISSN-L)
- 2373-7476
- OpenAlex
- W4402661169
- Cited by
- 10
- References
- 43
- Citations by year
- 2026: 3 · 2025: 7
- FWCI
- 3.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Computational Social Systems (publishedVersion)
- Funders
- Science and Technology Development Fund, Macao SAR (0093/2023/RIA2)
- National Natural Science Foundation of China (62176080)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- Industrial Internet of Things, cybersecurity, advanced persistent threats, attacker-defender game, reinforcement learning, cyber-physical attacks, social manufacturing
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