Research on Endogenous Security Defense for Cloud-Edge Collaborative Industrial Control Systems Based on Luenberger Observer

Lin Guan, Ci Tao and Ping Chen ()
Additional contact information
Lin Guan: College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200437, China
Ci Tao: College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200437, China
Ping Chen: College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200437, China

Mathematics, 2025, vol. 13, issue 17, 1-17

Abstract: Industrial Control Systems (ICSs) are fundamental to critical infrastructure, yet they face increasing cybersecurity threats, particularly data integrity attacks like replay and data forgery attacks. Traditional IT-centric security measures are often inadequate for the Operational Technology (OT) environment due to stringent real-time and reliability requirements. This paper proposes an endogenous security defense mechanism based on the Luenberger observer and residual analysis. By embedding a mathematical model of the physical process into the control system, this approach enables real-time state estimation and anomaly detection. We model the ICS using a linear state-space representation and design a Luenberger observer to generate a residual signal, which is the difference between the actual sensor measurements and the observer’s predictions. Under normal conditions, this residual is minimal, but it deviates significantly during a replay attack. We formalize the system model, observer design, and attack detection algorithm. The effectiveness of the proposed method is validated through a simulation of an ICS under a replay attack. The results demonstrate that the residual-based approach can detect the attack promptly and effectively, providing a lightweight yet robust solution for enhancing ICS security.

Keywords: industrial control systems (ICSs); endogenous security; Luenberger observer; replay attack; residual analysis; anomaly detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/17/2703/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/17/2703/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:17:p:2703-:d:1730338

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().