Journal
INFORMATION SCIENCES
Volume 547, Issue -, Pages 539-552Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.08.008
Keywords
State estimation; DoS attack; Data integrity attack; Statistical learning based detection
Categories
Funding
- National Natural Science Foundation of China [61973123, 61673177, 61673034]
- development fund for Shanghai talents
- Shanghai Natural Science Foundation [18ZR1409700]
- Programme of Introducing Talents of Discipline to Universities (the 111 Project) [B17017]
- Fundamental Research Funds for the Central Universities
Ask authors/readers for more resources
With the development of technology, cyber attacks have become more complex and intelligent, posing threats to system operation. Our study on secure estimation problem involves a distributed estimator and detector, exploring their relationship and performance impact, and demonstrating effectiveness through numerical examples.
With the development of digitization and intelligence of information technology, cyber attacks tend to be much more complicated and intelligent, which can disrupt the normal system operation if no any protection mechanism is implemented. Motivated by the security problem of industrial control system, we study secure estimation problem in which sensors are exposed to hostile communication environment, where the attacker can randomly launch either DoS attacks or data integrity attacks. We design a distributed estimator equipped with a statistical learning based detector for each sensor over wireless sensor network, and derive an optimal gain for the estimator. Moreover, we investigate the relationship between false rate and the chosen confident level of the detector, we also demonstrate the influence of sliding window of the detector on the estimation performance and show the existence of an optimal scaling parameter corresponding to the best estimation performance. Finally, we prove the effectiveness and feasibility of the proposed estimator by some numerical examples. (C) 2020 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available