Trustworthy Data Analysis and Sensor Data Protection in Cyber-Physical Systems
Abstract
Cyber-Physical Systems are widely used in critical infrastructures such as the power grids, water purification systems, nuclear plants, oil refinery and compressor plants, food manufacturing, etc. Anomalies in these systems can be a result of cybersecurity attacks, failed sensors or communication channels. Undetected anomalies may lead to process failure, cause financial damage and have significant impact on human lives. Thus, it is important to detect anomalies at early stages and to protect data in Cyber-Physical Systems. In this paper, we propose the novel on-the-fly NIST-compliant key generation scheme for a secure data container used to transfer and store sensor data. The data container delivers data from the low-level field sensors to high-level data analysis servers in a protected form. It provides data confidentiality and integrity, as well as data origin integrity, a fine-grained role-based and attribute-based access control. As a result, the anomaly detector runs on trustworthy data sets, protected from unauthorized adversarial modifications. Our solution can be easily integrated with many existing Cyber-Physical Systems and IT infrastructures since our secure data container supports RESTful API and is implemented in two modifications: (1) signed, watermarked and encrypted spreadsheet file; (2) signed and encrypted JSON file. In addition, we implemented several machine learning models based on a Random Forest, a k-Nearest Neighbors, a Support Vector Machine and a Neural Network algorithms for the detection of various anomalies and attacks in a gas pipeline system. We will demonstrate that our anomaly detection models achieve high detection rate with an average accuracy of 97.7% for two industrial data sets collected by the Mississippi State University's Critical Infrastructure Protection Center and Oak Ridge National Laboratories (ORNL)
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Correlation-based methods are particularly suitable for attacks and faults that selectively perturb a subset of variables while keeping other measurements within nominal ranges, and for scenarios where the key symptom is a collapse or distortion of inter-sensor correlation rather than a large deviation of a single signal.
“This makes them particularly suitable for attacks and faults that selectively perturb a subset of variables while keeping other measurements within nominal ranges, as well as for scenarios where the key symptom is a collapse or distortion of inter-sensor correlation rather than a large deviation of a single signal [94,96,99-101,106-108].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [100]
By focusing on joint behavior across multiple sensors and control channels, correlation-based detectors can better separate cyber-physical attacks (which often produce inconsistent or coordinated manipulation patterns) from natural faults (which follow typical failure correlations or fault signatures).
“By focusing on joint behavior across multiple sensors and control channels, correlation-based detectors can better separate cyber-physical attacks, which often produce inconsistent or coordinated manipulation patterns, from natural faults that follow typical failure correlations or fault signatures [91,95,96,100,101].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [100]
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Seo, J. K., Lee, J., Kim, B., Shim, W., & Seo, J. T. (2025) · Electronics · doi:10.3390/electronics15010020 · Cited by 15 · see citing works
- Authors
- Ulybyshev, D., Yilmaz, I., Northern, B., Kholodilo, V., & Rogers, M.
- Affiliations
- Tennessee Technological University (US)
- Venue
- Proceedings of the 2021 ACM Workshop on Secure and Trustworthy Cyber-Physical Systems
- Publisher
- ACM
- Published
- 2021-04-26
- Pages
- 13-22
- OpenAlex
- W3165256310
- Cited by
- 10
- References
- 26
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 2 · 2022: 2
- FWCI
- 2.61
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Workshop on Secure and Trustworthy Cyber-Physical Systems (SaT-CPS) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- cyber-physical systems, anomaly detection, data confidentiality, data integrity, attribute-based access control, role-based access control, gas pipelines, critical infrastructure protection, machine learning anomaly detection, data watermarking
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