The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Classification of Disturbances and Cyber-Attacks in Power Systems Using Heterogeneous Time-Synchronized Data
Abstract
Visualization and situational awareness are of vital importance for power systems, as the earlier a power-system event such as a transmission line fault or cyber-attack is identified, the quicker operators can react to avoid unnecessary loss. Accurate time-synchronized data, such as system measurements and device status, provide benefits for system state monitoring. However, the time-domain analysis of such heterogeneous data to extract patterns is difficult due to the existence of transient phenomena in the analyzed measurement waveforms. This paper proposes a sequential pattern mining approach to accurately extract patterns of power-system disturbances and cyber-attacks from heterogeneous time-synchronized data, including synchrophasor measurements, relay logs, and network event monitor logs. The term common path is introduced. A common path is a sequence of critical system states in temporal order that represent individual types of disturbances and cyber-attacks. Common paths are unique signatures for each observed event type. They can be compared to observed system states for classification. In this paper, the process of automatically discovering common paths from labeled data logs is introduced. An included case study uses the common path-mining algorithm to learn common paths from a fusion of heterogeneous synchrophasor data and system logs for three types of disturbances (in terms of faults) and three types of cyber-attacks, which are similar to or mimic faults. The case study demonstrates the algorithm's effectiveness at identifying unique paths for each type of event and the accompanying classifier's ability to accurately discern each type of event.
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“First, many approaches explicitly synchronize cyber-layer events with physical-layer behavior on a common time axis to detect cyber-physical inconsistencies that single-domain detectors would miss [122-129].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [125]
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Metadata
- Authors
- Pan, S., Morris, T., & Adhikari, U.
- Affiliations
- Mississippi State University (US)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2015-06
- Volume
- 11
- Issue
- 3
- Pages
- 650-662
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2020524857
- Cited by
- 180
- References
- 29
- Citations by year
- 2026: 19 · 2025: 16 · 2024: 17 · 2023: 30 · 2022: 17 · 2021: 16 · 2020: 17 · 2019: 19 · 2018: 7 · 2017: 16 · 2016: 4 · 2015: 2
- FWCI
- 4.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- Division of Undergraduate Education (1315726)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Engineering › Control and Systems Engineering › Power Systems Fault Detection
- Keywords
- cyberattack detection, sequential pattern mining, synchrophasor measurements, fault classification, situational awareness
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