Modeling Cascading Risk Dynamics in Governance, Risk, and Compliance Systems through Nonlinear Wave Profile Decomposition and Adaptive Control Optimization

  • Authors

    https://doi.org/10.14419/8aed0265

    Received date: June 16, 2025

    Accepted date: January 22, 2026

    Published date: February 12, 2026

  • Cascading Risk Modeling, Nonlinear Wave Decomposition, Cybersecurity, Risk Propagation, Adaptive Controls, Mitigation Strategies, IoT Ecosystems, Real-Time Risk Management, Cyberattack Simulation, Dynamic Risk Assessment

  • Abstract

     This paper presents a novel approach to cybersecurity risk management through the development of a cascading risk model that simulates the propagation of risks across interconnected systems and evaluates mitigation strategies. Unlike traditional linear risk models, the proposed model integrates nonlinear wave decomposition and adaptive control mechanisms to capture the dynamic and evolving nature of cybersecurity threats. The model was validated using data from both the Kaggle Cyberattack Dataset and the National Vulnerability Database (NVD), demonstrating its ability to accurately model cascading risks and assess the effectiveness of mitigation measures. The simulation results show significant reductions in risk propagation, with mitigation strategies reducing the overall risk by up to 42% for certain attack types. The findings underscore the model’s ability to address gaps in existing cybersecurity frameworks by providing real-time adaptive risk management in complex, interdependent environments. This work offers a scalable solution for improving cybersecurity resilience in modern infrastructure and IoT ecosystems.

  • References

    1. K. C. Chaganti, “A Scalable, Lightweight AI-Driven Security Framework for IoT Ecosystems: Optimization and Game Theory Approaches,” IEEE Access, vol. 99, pp. 1–1, 2025. [Online]. Available: https://doi.org/10.1109/ACCESS.2025.3558623
    2. A. Yaseen, ”Enhancing Cybersecurity through Automated Infrastructure Management: A Comprehensive Study on Optimizing Security Measures,” Quarterly Journal of Emerging Technologies, 2024. Available: Available: https://www.researchgate.net/.../ Enhancing-Cybersecurity-through-Automated-Infrastructure-Management-A-Comprehensive-Study-on-Optimizing-Security-Measures.pdf
    3. ¨ O. Aslan, S. S. Aktu˘g, M. Ozkan-Okay, A. A. Yilmaz, and E. Akin, “A Comprehensive Review of Cybersecurity Vulnerabilities, Threats, Attacks, and Solutions,” Electronics, vol. 12, no. 6, p. 1333, 2023. Available: https://www.mdpi.com/2079-9292/12/6/1333
    4. S. Abraham and S. Nair, “Comparative analysis and patch optimization using the cybersecurity analytics framework,” The Journal of Defense Modeling and Simulation, vol. 15, no. 3, pp. 281–292, 2018. Available: https://journals.sagepub.com/doi/abs/10.1177/1548512917705743
    5. N. Doukas, “Reliability-Based Design Optimization for Cyber Survivability,” in Reliability Assessment and Optimization of Complex Systems, Elsevier, 2025, pp. 615–639. Available: https://www.sciencedirect.com/science/article/pii/B9780443291128000293 International Journal of Applied Mathematical Research 11
    6. T. Kure, S. Islam, M. Ghazanfar, and A. Raza, “Asset criticality and risk prediction for an effective cybersecurity risk management of cyber-physical system,” Neural Computing and Applications, vol. 34, pp. 2259–2275, 2022. Available: https://link.springer.com/article/10.1007/s00521-022-06959-2
    7. H. M. Elavarasan, R. Manoharan, S. Murlidharan, and J. Karnati, “Battery Management System: Threat Modeling, Vulnerability Analysis, and Cybersecurity Strategy,” IEEE Access, vol. 9, pp. 5176–5184, 2021. Available: https://ieeexplore.ieee.org/document/9447569
    8. L. Judijanto and D. Hindarto, “Edge of enterprise architecture in addressing cyber security threats and business risks,” International Journal of Security and Computer Science, vol. 12, no. 6, pp. 118–132, 2023. Available: http://www.journal.lembagakita.org/index.php/ijsecs/article/view/1816
    9. M. Ficco, M. Chora´s, and R. Kozik, “Simulation Platform for Cyber-Security and Vulnerability Analysis of Critical Infrastructures,” Procedia Computer Science, vol. 108, pp. 1948–1957, 2017. Available: https://doi.org/10.1016/j.procs.2017.05.142
    10. H. I. Kure, S. Islam, and M. Ghazanfar, “Cybersecurity risk assessment in smart city infrastructures,” Journal of Computational Science, vol. 17, pp. 163–175, 2022. Available: https://www.sciencedirect.com/science/article/pii/S187775032100147X
    11. N. Doukas and T. Poleto, “Reliability-Based Design Optimization for Cyber-Physical Systems in the Context of Cyber Survivability,” in Reliabil ity Assessment and Optimization of Complex Systems, Elsevier, 2025, pp. 122–135. Available: https://www.sciencedirect.com/science/article/pii/ B9780443291128000165
    12. S. Abraham, “Optimization Models for Cyber Infrastructure Security and Their Impact on Mitigating Risks,” IEEE Access, vol. 11, pp. 1239–1249, 2023. Available: https://ieeexplore.ieee.org/document/9867201
    13. Z. Zhou et al., “Modeling Cascading Failures in Interdependent Critical Infrastructures,” Complexity, vol. 2023, Article ID 1371526, 2023. Available: https://www.hindawi.com/journals/complexity/2023/1371526
    14. Y. Guo, J. Wang, and M. Li, “Dynamic Risk Modeling in Cloud Computing with Adaptive Feedback,” IEEE Transactions on Cloud Computing, vol. 10, no. 2, pp. 125–136, 2022. Available: https://ieeexplore.ieee.org/document/9520112
    15. M.Hossain and A. El Saddik, “Time-Series Decomposition-Based Cyber Risk Estimation,” IEEE Internet of Things Journal, vol. 8, no. 3, pp. 1874–1883, 2021. Available: https://ieeexplore.ieee.org/document/9247164
    16. X. Zhang, L. He, and H. Wang, “Cascading Risks and Mitigation Strategies in Large-Scale Systems,” International Journal of Critical Infrastructure Protection, vol. 36, p. 100459, 2021. Available: https://doi.org/10.1016/j.ijcip.2021.100459
    17. F. Li and R. Buyya, “Resource-Aware and Risk-Sensitive Workload Orchestration in GRC Cloud Environments,” Future Generation Computer Systems, vol. 135, pp. 67–80, 2022. Available: https://doi.org/10.1016/j.future.2022.04.008
    18. R. Yang and L. He, “A Control-Theoretic Approach to GRC-Based Cyber Risk Adaptation,” Journal of Systems and Software, vol. 182, p. 111073, 2022. Available: https://doi.org/10.1016/j.jss.2021.111073
    19. J. Kim and Y. Park, “Profile Decomposition of Nonlinear Systems for Cyber Risk Forecasting,” Journal of Network and Computer Applications, vol. 2023, p. 103619, 2023. Available: https://www.sciencedirect.com/science/article/pii/S1084804523000410
    20. A. Yaseen, “Enhancing Cybersecurity Through Automated Infrastructure Optimization,” ResearchGate, Preprint, 2024. Available: https://www. researchgate.net/publication/378594258
    21. L. Xu et al., “Nonlinear Wave-Based Risk Propagation Analysis in Complex Systems,” Chaos, Solitons & Fractals, vol. 165, p. 112905, 2023. Available: https://doi.org/10.1016/j.chaos.2022.112905

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  • How to Cite

    Paidy, P. (2026). Modeling Cascading Risk Dynamics in Governance, Risk, and Compliance Systems through Nonlinear Wave Profile Decomposition and Adaptive Control Optimization. International Journal of Applied Mathematical Research, 15(1), 18-30. https://doi.org/10.14419/8aed0265