Data-Driven Correlation of Cyber and Physical Anomalies for Holistic System Health Monitoring
Concerns of cyber-security threats are increasingly becoming a part of everyday operations of cyber-physical systems, especially in the context of critical infrastructures. However, despite the tight integration of cyber and physical components in modern critical infrastructures, the monitoring of cyber and physical subsystems is still done separately. For successful health monitoring of such systems, a holistic approach is needed. This paper presents an approach for holistic health monitoring of cyber-physical systems based on cyber and physical anomaly detection and correlation. We provide a data-driven approach for the detection of cyber and physical anomalies based on machine learning. The benefits of the presented approach are: 1) integrated architecture that supports the acquisition and real-time analysis of both cyber and physical data; 2) a metric for holistic health monitoring that allows for differentiation between physical faults, cyber intrusion, and cyber-physical attacks. We present experimental analysis on a power-grid use case using the IEEE-33 bus model. The system was tested on several types of attacks such as network scan, Denial of Service (DOS), and malicious command injections.
- Research Organization:
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; Commonwealth Cyber Initiative
- Grant/Contract Number:
- 0008775; AC07-05ID14517
- OSTI ID:
- 1833104
- Alternate ID(s):
- OSTI ID: 1833105; OSTI ID: 1879210
- Report Number(s):
- INL/JOU-21-62200-Rev000; 9627907
- Journal Information:
- IEEE Access, Journal Name: IEEE Access Vol. 9; ISSN 2169-3536
- Publisher:
- Institute of Electrical and Electronics EngineersCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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