Elaris Computing Nexus
Multi-Objective Game Theoretic Optimization Model for Autonomous Cyber Attack Mitigation in Heterogeneous Network Architectures
Elaris Computing Nexus
Received On : 26 May 2026
Revised On : 25 July 2026
Accepted On : 20 September 2026
Published On : 02 October 2026
Volume 02, 2026
Pages : 226-239
Abstract
The introduction of cloud, edge, Internet of Things (IoT), software-defined networking (SDN), cyber-physical systems (CPS), and wireless infrastructures has enlarged the cyber threat landscape, which requires new defense techniques to respond to adaptive and coordinated attacks. A Multi-Objective Game-Theoretic Optimization Model (MGTOM) is proposed in this paper for the problem of autonomous cyber-attack mitigation in heterogeneous network environments. The attacker-defender interaction is represented as a dynamic non-cooperative game and both sides simultaneously optimize attack impact, mitigation latency, resource utilization, defense effectiveness and network resilience. The utility-based payoff function and adaptive, strategy-selection algorithm allow autonomous defense responses based on attack severity and available computer resources. The accuracy of controlled simulation-based evaluation is high, with an accuracy of 95.08% and a macro-average AUC score of 0.969. The proposed model achieves an average success rate of mitigations of 93.23% and an effectiveness of 87.8% under the critical level of attack severity. The model takes 55.8 ms to make mitigation decisions at the network level of 800 nodes, and has lower computational and resource overhead than the evaluated baseline methods. The Pareto-based analysis also shows that there is a satisfying compromise between effectiveness of mitigation and time for decision-making. The results show that MGTOM offers a complete and scalable framework to mitigate cyber-attacks autonomously in a heterogeneous network environment.
Keywords
Multi-Objective Optimization, Game Theory, Autonomous Cyber-Attack Mitigation, Heterogeneous Network Architectures, Intelligent Cybersecurity.
- M. Wang, J. Liu, Y. Li, E. Tian, and C. Peng, “A comprehensive survey of game-theoretic approaches for networked systems,” Systems Science & Control Engineering, vol. 14, no. 1, Jan. 2026, doi: 10.1080/21642583.2026.2622131.
- Aljumah, T. A. Ahanger, and A. Zhumadillayeva, “Game-theoretic security optimization in UAV networks through AI-enabled digital twin intelligence,” Scientific Reports, Jul. 2026, doi: 10.1038/s41598-026-63521-z.
- S. K. Zelani, K. V. S. S. Ramakrishna, F. Asiri, A. Alyahya, and S. Basheer, “A reinforcement learning–driven payoff-adaptive game-theoretic framework for secure and reliable operation of mobile ad hoc networks,” Transactions on Emerging Telecommunications Technologies, vol. 37, no. 4, Mar. 2026, doi: 10.1002/ett.70394.
- Alajmi, A. Ghandoura, A. Motwakel, and G. Abdelhady, “Game-theoretic adaptive routing and integrated security framework for multi-hop LoRaWAN networks,” IEEE Internet of Things Journal, pp. 1–1, 2026, doi: 10.1109/JIOT.2026.3675915.
- Y. Wang, X. Liu, and X. Yu, “Research on joint game-theoretic modeling of network attack and defense under incomplete information,” Entropy, vol. 27, no. 9, p. 892, Aug. 2025, doi: 10.3390/e27090892.
- N. Liu, Q.-W. Chai, S. Liu, F. Meng, and W.-M. Zheng, “Mixed strategy analysis in attack-defense game model based on 5G heterogeneous network of CPS using ncPSO,” Security and Communication Networks, vol. 2022, pp. 1–15, Nov. 2022, doi: 10.1155/2022/1181398.
- E. F. Siddiqui, M. Haleem, S. F. Ahmad, A. Salhi, A. T. Zamani, and N. Varish, “A multi-layered AI-driven cybersecurity architecture: Integrating entropy analytics, fuzzy reasoning, game theory, and multi-agent reinforcement learning for adaptive threat defense,” IEEE Access, vol. 13, pp. 170235–170257, 2025, doi: 10.1109/ACCESS.2025.3610526.
- Z. Han et al., “Smart optimization solution for channel access attack defense under UAV-aided heterogeneous network,” IEEE Internet of Things Journal, vol. 10, no. 21, pp. 18890–18897, Nov. 2023, doi: 10.1109/JIOT.2023.3281942.
- H. Mejdi, S. Elmadssia, M. Koubãa, and T. Ezzedine, “A comprehensive survey on game theory applications in cyber-physical system security: Attack models, security analyses, and machine learning classifications,” IEEE Access, vol. 12, pp. 163638–163653, 2024, doi: 10.1109/ACCESS.2024.3491502.
- M. F. Saiyed and I. Al-Anbagi, “A game theoretic model for strategic defence selection against DDoS attacks in IoT networks,” IEEE Transactions on Network and Service Management, vol. 22, no. 5, pp. 4509–4524, Oct. 2025, doi: 10.1109/TNSM.2025.3589901.
- R. Ranpara, S. K. Patel, O. P. Kumar, and F. A. Al-Zahrani, “Scalable architecture for autonomous malware detection and defense in software-defined networks using federated learning approaches,” Scientific Reports, vol. 15, no. 1, Aug. 2025, doi: 10.1038/s41598-025-14512-z.
- Petrillo, A. Pescape, and S. Santini, “A secure adaptive control for cooperative driving of autonomous connected vehicles in the presence of heterogeneous communication delays and cyberattacks,” IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1134–1149, Mar. 2021, doi: 10.1109/TCYB.2019.2962601.
- L. Khoshnevisan and X. Liu, “A secure adaptive resilient neural network-based control of heterogeneous connected automated vehicles subject to cyber attacks,” IEEE Transactions on Vehicular Technology, vol. 74, no. 6, pp. 8734–8744, Jun. 2025, doi: 10.1109/TVT.2025.3537869.
- J. Du et al., “Anti-jamming resource allocation in air-terrestrial integrated networks: A hierarchical game-theoretic MADRL approach,” IEEE Transactions on Cognitive Communications and Networking, vol. 12, pp. 5380–5392, 2026, doi: 10.1109/TCCN.2026.3657145.
- M. M. Trigui, M. S. Islam, M. D. Gambo, M. S. Alam, and T. Helmy, “Resource-aware ensemble framework for DDoS detection in SDN via novel game-theoretic feature selection,” IEEE Access, vol. 14, pp. 1666–1676, 2026, doi: 10.1109/ACCESS.2025.3618519.
- X. Zhao, J. Guo, J. Liu, and W. Huang, “Game-theoretic and inverse reinforcement learning-based control for vehicle formation under DoS attacks,” IEEE Internet of Things Journal, pp. 1–1, 2026, doi: 10.1109/JIOT.2026.3710354.
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The authors would like to thank the institution for providing the necessary facilities and support for this research.
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The data supporting the findings of this study are based on the CICIDS2017 dataset and controlled simulation results generated using the proposed Multi-Objective Game-Theoretic Optimization Model. The CICIDS2017 dataset is publicly available through the Canadian Institute for Cybersecurity. The simulation parameters, derived experimental results, and additional data generated during the study are available from the corresponding author upon reasonable request.
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Cite this Article
Hyun Jong Yang, “Multi-Objective Game Theoretic Optimization Model for Autonomous Cyber Attack Mitigation in Heterogeneous Network Architectures”, Elaris Computing Nexus, pp. 226-239, 2026, doi: 10.65148/ECN/2026019.
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© 2026 Hyun Jong Yang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.