Journal article
Stochastic coding detection scheme in cyber-physical systems against replay attack
Information sciences, Vol.481, pp.432-444
05/2019
DOI: 10.1016/j.ins.2018.12.091
Abstract
•A stochastic coding scheme is proposed to generate covariance difference of the compromised data under replay attack, which removes the performance degradations in existing works.•A residual-based detector is designed to detect the covariance changes caused by the coding scheme subject to the false alarm rate (FAR) and attack detection rate (ADR) limitations. Then, an output-based detector is proposed, which effectively reduce the design complexity of the residual-based detector.•Different from traditional detector, the above detectors apply a novel form to remove the non-convex constrain conditions in design optimal problems.
In this paper, the security problems in cyber-physical systems (CPSs) against replay attack are considered. With replay attacks, attacker records and covers the transmitted data between the senders and receivers of the sensors. In order to achieve the detection objective for malicious replay attacks, the stochastic coding scheme is proposed to make the CPSs generate covariance differences between the normal and compromised data. Different from the existing results, this method detects the replay attack without sacrificing any system performances in normal systems. Based on the coding scheme, two types of detectors are further designed to detect the covariance changes in residual and output, respectively. Moreover, the output-based detector has more low-computing solution process than the residual-based one. Finally, a practical example is proposed to demonstrate the superiority of the stochastic coding scheme.
Details
- Title: Subtitle
- Stochastic coding detection scheme in cyber-physical systems against replay attack
- Creators
- Dan Ye - Northeastern UniversityTian-Yu Zhang - College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, 110819 ChinaGe Guo - Northeastern UniversityTianyu Zhang - Computer Science
- Resource Type
- Journal article
- Publication Details
- Information sciences, Vol.481, pp.432-444
- Publisher
- Elsevier Inc
- DOI
- 10.1016/j.ins.2018.12.091
- ISSN
- 0020-0255
- eISSN
- 1872-6291
- Grant note
- 61773097, U1813214 / National Natural Science Foundation of China (https://doi.org/10.13039/501100001809)
- Language
- English
- Date published
- 05/2019
- Academic Unit
- Computer Science
- Record Identifier
- 9984696576902771
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