Deriving optimal defensive resource deployment of complex system-of-systems for risk response considering cascading effect

References

• Ackoff, R. L. (1971). Towards a system of systems concepts. Management Science, 17(11), 661–671. https://doi.org/10.1287/mnsc.17.11.661
   Web of Science ®Google Scholar
• Asfaw, Z. G., & Lindqvist, B. H. (2015). Unobserved heterogeneity in the power law nonhomogeneous Poisson process. Reliability Engineering & System Safety, 134, 59–65. https://doi.org/10.1016/j.ress.2014.10.005
   Web of Science ®Google Scholar
• Aziz, R. A., He, M., & Zhuang, J. (2020). An attacker–defender resource allocation game with substitution and complementary effects. Risk Analysis: An Official Publication of the Society for Risk Analysis, 40(7), 1481–1506. https://doi.org/10.1111/risa.13483
   PubMed Web of Science ®Google Scholar
• Barbierato, L., Pons, E., Bompard, E. F., Rajkumar, V. S., Palensky, P., Bottaccioli, L., & Patti, E. (2023). Exploring stability and accuracy limits of distributed real-time power system simulations via system-of-systems cosimulation. IEEE Systems Journal, 17(2), 3354–3365. https://doi.org/10.1109/JSYST.2022.3230092
   Web of Science ®Google Scholar
• Cappanera, P., & Scaparra, M. P. (2011). Optimal allocation of protective resources in shortest-path networks. Transportation Science, 45(1), 64–80. https://doi.org/10.1287/trsc.1100.0340
   Web of Science ®Google Scholar
• Chen, Z., Hong, D., Cui, W., Xue, W., Wang, Y., & Zhong, J. (2023). Resilience evaluation and optimal design for weapon system of systems with dynamic reconfiguration. Reliability Engineering & System Safety, 237, 109409. https://doi.org/10.1016/j.ress.2023.109409
   Web of Science ®Google Scholar
• Chen, R., Zhang, C., Wang, S., Zio, E., Dui, H., & Zhang, Y. (2023). Importance measures for critical components in complex system based on copula hierarchical Bayesian network. Reliability Engineering & System Safety, 230, 108883. https://doi.org/10.1016/j.ress.2022.108883
   Web of Science ®Google Scholar
• Chiaradonna, S., & Lanchier, N. (2022). Exact insurance premiums for cyber risk of small and medium-sized enterprises. Mathematical Modelling of Natural Phenomena, 17, 40. https://doi.org/10.1051/mmnp/2022041
   Web of Science ®Google Scholar
• Dong, C., & Zhao, L. (2019). Sensor network security defense strategy based on attack graph and improved binary PSO. Safety Science, 117, 81–87. https://doi.org/10.1016/j.ssci.2019.04.007
   Web of Science ®Google Scholar
• Feng, X., He, S., Chen, X., & Li, G. (2021). Mitigating the vulnerability of an air-high-speed railway transportation network: From the perspective of predisruption response. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 235(3), 474–490. https://doi.org/10.1177/1748006X20966090
   Web of Science ®Google Scholar
• Finkelstein, M. (2011). On the ‘rate of aging’ in heterogeneous populations. Mathematical Biosciences, 232(1), 20–23. https://doi.org/10.1016/j.mbs.2011.03.012
   PubMed Web of Science ®Google Scholar
• Fontaine, P., Crainic, T. G., Jabali, O., & Rei, W. (2021). Scheduled service network design with resource management for two-tier multimodal city logistics. European Journal of Operational Research, 294(2), 558–570. https://doi.org/10.1016/j.ejor.2021.02.009
   Web of Science ®Google Scholar
• Gao, K., Xiao, H., Qu, L., & Wang, S. (2022). Optimal interception strategy of air defence missile system considering multiple targets and phases. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 236(1), 138–147. https://doi.org/10.1177/1748006X211022111
   Web of Science ®Google Scholar
• Geng, S., Liu, S., & Fang, Z. (2021). Resilient communication model for satellite networks using clustering technique. Reliability Engineering & System Safety, 215, 107850. https://doi.org/10.1016/j.ress.2021.107850
   Web of Science ®Google Scholar
• Geng, S., Liu, S., & Fang, Z. (2022). An agent-based algorithm for dynamic routing in service networks. European Journal of Operational Research, 303(2), 719–734. https://doi.org/10.1016/j.ejor.2022.03.010
   Web of Science ®Google Scholar
• Ghorbani-Renani, N., González, A. D., Barker, K., & Morshedlou, N. (2020). Protection-interdiction-restoration: Tri-level optimization for enhancing interdependent network resilience. Reliability Engineering & System Safety, 199, 106907. https://doi.org/10.1016/j.ress.2020.106907
   Web of Science ®Google Scholar
• Glitho, R. H. (1994). Signalling system number 7 network services part and X.25: A comparative study. Computer Networks and ISDN Systems, 26(12), 1495–1502. https://doi.org/10.1016/0169-7552(94)90024-8
   Web of Science ®Google Scholar
• Haimes, Y. Y. (2018). Risk modeling of interdependent complex systems of systems: Theory and practice. Risk Analysis: An Official Publication of the Society for Risk Analysis, 38(1), 84–98. https://doi.org/10.1111/risa.12804
   PubMed Web of Science ®Google Scholar
• Han, Q., Pang, B., Li, S., Li, N., Guo, P-s., Fan, C-l., & Li, W-m (2023). Evaluation method and optimization strategies of resilience for air & space defense system of systems based on kill network theory and improved self-information quantity. Defence Technology, 21, 219–239. https://doi.org/10.1016/j.dt.2023.01.005
   Web of Science ®Google Scholar
• Hausken, K. (2014). Individual versus overarching protection and attack of assets. Central European Journal of Operations Research, 22(1), 89–112. https://doi.org/10.1007/s10100-012-0271-6
   Web of Science ®Google Scholar
• Hausken, K., & Levitin, G. (2009). Parallel systems with different types of defence resource expenditure under two sequential attacks. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 223(1), 71–85. https://doi.org/10.1243/1748006XJRR223
   Google Scholar
• Jin, J. G., Lu, L., Sun, L., & Yin, J. (2015). Optimal allocation of protective resources in urban rail transit networks against intentional attacks. Transportation Research Part E: Logistics and Transportation Review, 84, 73–87. https://doi.org/10.1016/j.tre.2015.10.008
   Web of Science ®Google Scholar
• Levitin, G. (2008). Optimizing defense strategies for complex multi-state systems. In V. M. Bier & M. N. Azaiez (Eds.), Game theoretic risk analysis of security threats (pp. 33–64). Springer.
   Google Scholar
• Levitin, G., & Hausken, K. (2009). Intelligence and impact contests in systems with fake targets. Defense & Security Analysis, 25(2), 157–173. https://doi.org/10.1080/14751790902985181
   Google Scholar
• Levitin, G., & Hausken, K. (2012). Individual versus overarching protection against strategic attacks. Journal of the Operational Research Society, 63(7), 969–981. https://doi.org/10.1057/jors.2011.96
   Web of Science ®Google Scholar
• Li, J., Ge, B., Jiang, J., Yang, K., & Chen, Y. (2020). High-end weapon equipment portfolio selection based on a heterogeneous network model. Journal of Global Optimization, 78(4), 743–761. https://doi.org/10.1007/s10898-018-0687-1
   Web of Science ®Google Scholar
• Li, J., Jiang, J., Yang, K., & Chen, Y. (2018). Research on functional robustness of heterogeneous combat networks. IEEE Systems Journal, 13(2), 1487–1495. https://doi.org/10.1109/JSYST.2018.2828779
   Web of Science ®Google Scholar
• Lin, M., Chen, T., Chen, H., Ren, B., & Zhang, M. (2023). When architecture meets ai: A deep reinforcement learning approach for system of systems design. Advanced Engineering Informatics, 56, 101965. https://doi.org/10.1016/j.aei.2023.101965
   Web of Science ®Google Scholar
• Lindqvist, B. H. (2008). On the statistical modeling and analysis of repairable systems. Quality Control and Applied Statistics, 53(1), 97–100.
   Google Scholar
• Lin, C., Xiao, H., Kou, G., & Peng, R. (2020). Defending a series system with individual protection, overarching protection, and disinformation. Reliability Engineering & System Safety, 204, 107131. https://doi.org/10.1016/j.ress.2020.107131
   Web of Science ®Google Scholar
• Lin, C., Xiao, H., Peng, R., & Xiang, Y. (2021). Optimal defense-attack strategies between m defenders and n attackers: A method based on cumulative prospect theory. Reliability Engineering & System Safety, 210, 107510. https://doi.org/10.1016/j.ress.2021.107510
   Web of Science ®Google Scholar
• Li, J., Tan, Y., Yang, K., Zhang, X., & Ge, B. (2017). Structural robustness of combat networks of weapon system-of-systems based on the operation loop. International Journal of Systems Science, 48(3), 659–674. https://doi.org/10.1080/00207721.2016.1212429
   Web of Science ®Google Scholar
• Liu, X., Vatn, J., Dijoux, Y., & Toftaker, H. (2020). Unobserved heterogeneity in stable imperfect repair models. Reliability Engineering & System Safety, 203, 107039. https://doi.org/10.1016/j.ress.2020.107039
   Web of Science ®Google Scholar
• Lv, H., & Li, D. (2021). Impacts of heterogeneous green consumers on green innovation in electric vehicle and charging pile firms. Sustainable Production and Consumption, 28, 1216–1231. https://doi.org/10.1016/j.spc.2021.08.002
   Web of Science ®Google Scholar
• Meng, S., Wiens, M., & Schultmann, F. (2019). Adversarial risks in the lab–an experimental study of framing-effects in attacker-defender games. Safety Science, 120, 551–560. https://doi.org/10.1016/j.ssci.2019.08.004
   Web of Science ®Google Scholar
• Ouyang, M., Xu, M., Zhang, C., & Huang, S. (2017). Mitigating electric power system vulnerability to worst-case spatially localized attacks. Reliability Engineering & System Safety, 165, 144–154. https://doi.org/10.1016/j.ress.2017.03.031
   Web of Science ®Google Scholar
• Payyappalli, V. M., Zhuang, J., & Jose, V. R. R. (2017). Deterrence and risk preferences in sequential attacker–defender games with continuous efforts. Risk Analysis: An Official Publication of the Society for Risk Analysis, 37(11), 2229–2245. https://doi.org/10.1111/risa.12768
   PubMed Web of Science ®Google Scholar
• Peng, R., He, X., Zhong, C., Kou, G., & Xiao, H. (2022). Preventive maintenance for heterogeneous parallel systems with two failure modes. Reliability Engineering & System Safety, 220, 108310. https://doi.org/10.1016/j.ress.2021.108310
   Web of Science ®Google Scholar
• Peng, C. Y., & Tseng, S. T. (2009). Mis-specification analysis of linear degradation models. IEEE Transactions on Reliability, 58(3), 444–455.
   Web of Science ®Google Scholar
• Peng, R., Zhai, Q., & Levitin, G. (2016). Defending a single object against an attacker trying to detect a subset of false targets. Reliability Engineering & System Safety, 149, 137–147. https://doi.org/10.1016/j.ress.2016.01.002
   Web of Science ®Google Scholar
• Raman, R., & D'Souza, M. (2019). Decision learning framework for architecture design decisions of complex systems and system-of-systems. Systems Engineering, 22(6), 538–560. https://doi.org/10.1002/sys.21517
   Web of Science ®Google Scholar
• Shao, C., & Li, Y.-F. (2022). Multistage attack–defense graph game analysis for protection resources allocation optimization against cyber attacks considering rationality evolution. Risk Analysis: An Official Publication of the Society for Risk Analysis, 42(5), 1086–1105. https://doi.org/10.1111/risa.13837
   PubMed Web of Science ®Google Scholar
• Sun, F., Fu, F., Liao, H., & Xu, D. (2020). Analysis of multivariate dependent accelerated degradation data using a random-effect general wiener process and d-vine copula. Reliability Engineering & System Safety, 204, 107168. https://doi.org/10.1016/j.ress.2020.107168
   Web of Science ®Google Scholar
• Sun, Q., Li, H., Wang, Y., & Zhang, Y. (2022). Multi-swarm-based cooperative reconfiguration model for resilient unmanned weapon system-of-systems. Reliability Engineering & System Safety, 222, 108426. https://doi.org/10.1016/j.ress.2022.108426
   Web of Science ®Google Scholar
• Volovski, M., Murillo-Hoyos, J., Saeed, T. U., & Labi, S. (2017). Estimation of routine maintenance expenditures for highway pavement segments: Accounting for heterogeneity using random-effects models. Journal of Transportation Engineering, Part A: Systems, 143(5), 04017006. https://doi.org/10.1061/JTEPBS.0000041
   Web of Science ®Google Scholar
• Wang, X. (2010). Wiener processes with random effects for degradation data. Journal of Multivariate Analysis, 101(2), 340–351. https://doi.org/10.1016/j.jmva.2008.12.007
   Web of Science ®Google Scholar
• Wang, Z., Liu, S., & Fang, Z. (2019). Research on SoS-GERT network model for equipment system of systems contribution evaluation based on joint operation. IEEE Systems Journal, 14(3), 4188–4196. https://doi.org/10.1109/JSYST.2019.2956549
   Web of Science ®Google Scholar
• Wang, Z., Zhai, Q., & Chen, P. (2021). Degradation modeling considering unit-to-unit heterogeneity – A general model and comparative study. Reliability Engineering & System Safety, 216, 107897. https://doi.org/10.1016/j.ress.2021.107897
   Web of Science ®Google Scholar
• Wen, Y., Wu, J., Das, D., & Tseng, T.-L. B. (2018). Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity. Reliability Engineering & System Safety, 176, 113–124. https://doi.org/10.1016/j.ress.2018.04.005
   Web of Science ®Google Scholar
• Wu, Y., Chen, Z., Gong, H., Feng, Q., Chen, Y., & Tang, H. (2021). Defender–attacker–operator: Tri-level game-theoretic interdiction analysis of urban water distribution networks. Reliability Engineering & System Safety, 214, 107703. https://doi.org/10.1016/j.ress.2021.107703
   Web of Science ®Google Scholar
• Wu, D., Yan, X., Peng, R., & Wu, S. (2020). Optimal defence-attack strategies between one defender and two attackers. Journal of the Operational Research Society, 71(11), 1830–1846. https://doi.org/10.1080/01605682.2019.1630332
   Web of Science ®Google Scholar
• Xu, S., Xia, Y., & Ouyang, M. (2020). Effect of resource allocation to the recovery of scale-free networks during cascading failures. Physica A: Statistical Mechanics and Its Applications, 540, 123157. https://doi.org/10.1016/j.physa.2019.123157
   Web of Science ®Google Scholar
• Xu, Z., & Zhuang, J. (2019). A study on a sequential one-defender-n-attacker game. Risk Analysis: An Official Publication of the Society for Risk Analysis, 39(6), 1414–1432. https://doi.org/10.1111/risa.13257
   PubMed Web of Science ®Google Scholar
• Yu, D., Wu, J., Wang, W., & Gu, B. (2022). Optimal performance of hybrid energy system in the presence of electrical and heat storage systems under uncertainties using stochastic p-robust optimization technique. Sustainable Cities and Society, 83, 103935. https://doi.org/10.1016/j.scs.2022.103935
   Web of Science ®Google Scholar
• Zhang, X., Ding, K., Zhang, H., & Jiang, G. (2020, October 11–14). Incorporating risk preferences into a defense-attack game [Paper presentation]. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020), Toronto, ON, Canada (pp. 2887–2892). https://doi.org/10.1109/SMC42975.2020.9282870
   Google Scholar
• Zheng, H., Yang, J., & Zhao, Y. (2023). Reliability demonstration test plan for degraded products subject to gamma process with unit heterogeneity. Reliability Engineering & System Safety, 240, 109617. https://doi.org/10.1016/j.ress.2023.109617
   Web of Science ®Google Scholar
• Zhou, J., Huang, N., Coit, D. W., & Felder, F. A. (2018). Combined effects of load dynamics and dependence clusters on cascading failures in network systems. Reliability Engineering & System Safety, 170, 116–126. https://doi.org/10.1016/j.ress.2017.10.008
   Web of Science ®Google Scholar