Optimal allocation of defensive resources to defend urban power networks against different types of attackers
International Journal of Critical Infrastructure Protection, ISSN: 1874-5482, Vol: 35, Page: 100467
2021
- 9Citations
- 11Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
As an important part of urban lifeline networks, the urban power network is threatened by multiple types of intentional attackers, such as criminals, terrorists, illegal employees, etc. Each type of attackers differs remarkably in purposes, strategies and valuations of targets. In order to improve the defense capability of urban power network under the threat of different types of attackers, this study proposed an optimal allocation method of defensive resources based on Bayesian game model to reduce the expected loss of the defender. In the case study of IEEE 30-bus network, the reliability and applicability of the resource optimization allocation method are verified through the comparison of the expected loss under different strategy combinations. It is proved that the Bayesian Nash equilibrium defense strategy is the best choice for the defender when there are different types of attackers.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1874548221000548; http://dx.doi.org/10.1016/j.ijcip.2021.100467; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85111582154&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1874548221000548; https://dx.doi.org/10.1016/j.ijcip.2021.100467
Elsevier BV
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