Defending a cyber system with early warning mechanism
Creators
- 1. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu (China)
- 2. Department of Mathematics, Illinois State University, Normal, IL (United States)
- 3. Department of Computer Science, University of Houston, Houston, TX (United States)
Description
Highlights: • We consider an important mechanism of early warning for defending a cyber system that has become a key component of constructing network defense in practice. • The reliability function of a system under attack from one or multiple sources is derived. • We propose the optimal strategy for defending a cyber system with early warning components in the worst attack scenario. - Abstract: Due to the increasing reliance on networks, defending a cyber system is of vital importance. In this paper, we consider an important mechanism of early warning for defending a cyber system that has become a key component of constructing network defense in practice. We study the reliability of a system under attack from single or multiple sources. In particular, we discuss the effect of an early warning mechanism on the system reliability. We then propose the optimal strategy for defending a cyber system with early warning components in the worst attack scenario. The theoretical results are further validated by simulation evidence.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.ress.2017.08.021Additional details
Identifiers
- DOI
- 10.1016/j.ress.2017.08.021;
- PII
- S0951-8320(17)30105-9;
Publishing Information
- Journal Title
- Reliability Engineering and System Safety
- Journal Volume
- 169
- Journal Page Range
- p. 224-234
- ISSN
- 0951-8320
- CODEN
- RESSEP
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 49091358
- Subject category
- S42: ENGINEERING;
- Descriptors DEI
- NATIONAL DEFENSE; OPTIMIZATION; RELIABILITY
Optional Information
- Copyright
- Copyright (c) 2017 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.