Sequential Information Fusion for Resilient Estimation: Dynamic Observation Scheduling against Intermittent DoS Attacks

Jie Lu; Wenhao Lin; Chao Yang

doi:10.62762/CJIF.2025.922221

Volume 3, Issue 1, Chinese Journal of Information Fusion

Volume 3, Issue 1, 2026

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Jiayi Ma

Wuhan University, China

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Chinese Journal of Information Fusion, Volume 3, Issue 1, 2026: 46-61

Open Access | Research Article | 06 March 2026

Sequential Information Fusion for Resilient Estimation: Dynamic Observation Scheduling against Intermittent DoS Attacks

Jie Lu 1,2

Wenhao Lin 1,2

Chao Yang 1,2 *

1 Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai 200237, China

2 Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

* Corresponding Author: Chao Yang, [email protected]

DOI: 10.62762/CJIF.2025.922221

ARK: ark:/57805/cjif.2025.922221

Received: 06 November 2025, Accepted: 09 January 2026, Published: 06 March 2026

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Abstract

In this paper, we consider the state estimation problem in a cyber-physical system (CPS) against intermittent denial-of-service (DoS) attacks, which are usually difficult to defend due to their concealment and unpredictability. To address this issue, this paper proposes a dynamic observation scheduling method based on Fisher information to achieve efficient and resilient state estimation. Specifically, a sliding window mechanism is first employed to predict the successful transmission probability for each time window. Subsequently, the method constructs a scheduling sequence by aligning these predicted probabilities with the Fisher information of the observation's components. This strategy effectively achieves the co-optimization of message quality and transmission risk. Theoretical analysis shows that the scheduling method can be viewed as a risk-avoidance weighted maximization problem, which can achieve single-step optimality through same-direction matching. Simulations compare the proposed approach with fixed-order and random scheduling strategies. The results show that the proposed algorithm significantly improves the estimation accuracy under standard attacks and maintains effective estimation even under extreme attacks.

Graphical Abstract

Keywords

dynamic observation scheduling

fisher information

information fusion

intermittent DoS attacks

Data Availability Statement

Data will be made available on request.

Funding

This work was supported by the National Natural Science Foundation of China under Grant 62336005.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that generative AI tools were used in the preparation of this manuscript. Specifically, ChatGPT-5 and Deepseek-R1 were employed to polish the English writing of several paragraphs. The authors reviewed and edited the content as needed and take full responsibility for the final content of the publication.

Ethical Approval and Consent to Participate

Not applicable.

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Lu, J., Lin, W., & Yang, C. (2026). Sequential Information Fusion for Resilient Estimation: Dynamic Observation Scheduling against Intermittent DoS Attacks. Chinese Journal of Information Fusion, 3(1), 46–61. https://doi.org/10.62762/CJIF.2025.922221

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TY  - JOUR
AU  - Lu, Jie
AU  - Lin, Wenhao
AU  - Yang, Chao
PY  - 2026
DA  - 2026/03/06
TI  - Sequential Information Fusion for Resilient Estimation: Dynamic Observation Scheduling against Intermittent DoS Attacks
JO  - Chinese Journal of Information Fusion
T2  - Chinese Journal of Information Fusion
JF  - Chinese Journal of Information Fusion
VL  - 3
IS  - 1
SP  - 46
EP  - 61
DO  - 10.62762/CJIF.2025.922221
UR  - https://www.icck.org/article/abs/CJIF.2025.922221
KW  - dynamic observation scheduling
KW  - fisher information
KW  - information fusion
KW  - intermittent DoS attacks
AB  - In this paper, we consider the state estimation problem in a cyber-physical system (CPS) against intermittent denial-of-service (DoS) attacks, which are usually difficult to defend due to their concealment and unpredictability. To address this issue, this paper proposes a dynamic observation scheduling method based on Fisher information to achieve efficient and resilient state estimation. Specifically, a sliding window mechanism is first employed to predict the successful transmission probability for each time window. Subsequently, the method constructs a scheduling sequence by aligning these predicted probabilities with the Fisher information of the observation's components. This strategy effectively achieves the co-optimization of message quality and transmission risk. Theoretical analysis shows that the scheduling method can be viewed as a risk-avoidance weighted maximization problem, which can achieve single-step optimality through same-direction matching. Simulations compare the proposed approach with fixed-order and random scheduling strategies. The results show that the proposed algorithm significantly improves the estimation accuracy under standard attacks and maintains effective estimation even under extreme attacks.
SN  - 2998-3371
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Lu2026Sequential,
  author = {Jie Lu and Wenhao Lin and Chao Yang},
  title = {Sequential Information Fusion for Resilient Estimation: Dynamic Observation Scheduling against Intermittent DoS Attacks},
  journal = {Chinese Journal of Information Fusion},
  year = {2026},
  volume = {3},
  number = {1},
  pages = {46-61},
  doi = {10.62762/CJIF.2025.922221},
  url = {https://www.icck.org/article/abs/CJIF.2025.922221},
  abstract = {In this paper, we consider the state estimation problem in a cyber-physical system (CPS) against intermittent denial-of-service (DoS) attacks, which are usually difficult to defend due to their concealment and unpredictability. To address this issue, this paper proposes a dynamic observation scheduling method based on Fisher information to achieve efficient and resilient state estimation. Specifically, a sliding window mechanism is first employed to predict the successful transmission probability for each time window. Subsequently, the method constructs a scheduling sequence by aligning these predicted probabilities with the Fisher information of the observation's components. This strategy effectively achieves the co-optimization of message quality and transmission risk. Theoretical analysis shows that the scheduling method can be viewed as a risk-avoidance weighted maximization problem, which can achieve single-step optimality through same-direction matching. Simulations compare the proposed approach with fixed-order and random scheduling strategies. The results show that the proposed algorithm significantly improves the estimation accuracy under standard attacks and maintains effective estimation even under extreme attacks.},
  keywords = {dynamic observation scheduling, fisher information, information fusion, intermittent DoS attacks},
  issn = {2998-3371},
  publisher = {Institute of Central Computation and Knowledge}
}

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