Seeking best-balanced patch-injecting strategies through optimal control approach

Huang, Kaifan, Li, Pengdeng, Yang, Lu-Xing, Yang, Xiaofan and Tang, Yuan Yan 2019, Seeking best-balanced patch-injecting strategies through optimal control approach, Security and communication networks, vol. 2019, doi: 10.1155/2019/2315627.

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Title Seeking best-balanced patch-injecting strategies through optimal control approach
Author(s) Huang, Kaifan
Li, Pengdeng
Yang, Lu-XingORCID iD for Yang, Lu-Xing orcid.org/0000-0002-9229-5787
Yang, Xiaofan
Tang, Yuan Yan
Journal name Security and communication networks
Volume number 2019
Article ID 2315627
Total pages 12
Publisher Hindawi
Place of publication Cairo, Egypt
Publication date 2019-06
ISSN 1939-0114
1939-0122
Summary © 2019 Kaifan Huang et al. To restrain escalating computer viruses, new virus patches must be constantly injected into networks. In this scenario, the patch-developing cost should be balanced against the negative impact of virus. This article focuses on seeking best-balanced patch-injecting strategies. First, based on a novel virus-patch interactive model, the original problem is reduced to an optimal control problem, in which (a) each admissible control stands for a feasible patch-injecting strategy and (b) the objective functional measures the balance of a feasible patch-injecting strategy. Second, the solvability of the optimal control problem is proved, and the optimality system for solving the problem is derived. Next, a few best-balanced patch-injecting strategies are presented by solving the corresponding optimality systems. Finally, the effects of some factors on the best balance of a patch-injecting strategy are examined. Our results will be helpful in defending against virus attacks in a cost-effective way.
Language eng
DOI 10.1155/2019/2315627
Indigenous content off
Field of Research 0802 Computation Theory and Mathematics
0805 Distributed Computing
0899 Other Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, Kaifan Huang et al.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30126457

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