Descriptive Analytics-Based Anomaly Detection for Cybersecure Load Forecasting
Abstract
As power delivery systems evolve and become increasingly reliant on accurate forecasts, they will be more and more vulnerable to cybersecurity issues. A coordinated data attack by sophisticated adversaries can render existing data corrupt or outlier detection methods ineffective. This would have a very negative impact on operational decisions. The focus of this paper is to develop descriptive analytics-based methods for anomaly detection to protect the load forecasting process against cyberattacks to essential data. We propose an integrated solution (IS) and a hybrid implementation of IS (HIIS) that can detect and mitigate cyberattack induced long sequence anomalies. HIIS is also capable of improving true positive rates and reducing false positive rates significantly comparing with IS. The proposed HIIS can serve as an online cybersecure load forecasting scheme.
- Authors:
-
[1];
[3]
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Univ. of North Carolina, Charlotte, NC (United States)
- Southern Methodist Univ., Dallas, TX (United States)
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Electricity Delivery and Energy Reliability (OE), Power Systems Engineering Research and Development (OE-10)
- OSTI Identifier:
- 1601349
- Report Number(s):
- BNL-213641-2020-JAAM
Journal ID: ISSN 1949-3053
- Grant/Contract Number:
- SC0012704
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Transactions on Smart Grid
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 6; Journal ID: ISSN 1949-3053
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 24 POWER TRANSMISSION AND DISTRIBUTION; Load forecasting; Predictive models; Forecasting; Computer crime; Load modeling; Weather forecasting
Citation Formats
Yue, Meng, Hong, Tao, and Wang, Jianhui. Descriptive Analytics-Based Anomaly Detection for Cybersecure Load Forecasting. United States: N. p., 2019.
Web. doi:10.1109/TSG.2019.2894334.
Yue, Meng, Hong, Tao, & Wang, Jianhui. Descriptive Analytics-Based Anomaly Detection for Cybersecure Load Forecasting. United States. https://doi.org/10.1109/TSG.2019.2894334
Yue, Meng, Hong, Tao, and Wang, Jianhui. Fri .
"Descriptive Analytics-Based Anomaly Detection for Cybersecure Load Forecasting". United States. https://doi.org/10.1109/TSG.2019.2894334. https://www.osti.gov/servlets/purl/1601349.
@article{osti_1601349,
title = {Descriptive Analytics-Based Anomaly Detection for Cybersecure Load Forecasting},
author = {Yue, Meng and Hong, Tao and Wang, Jianhui},
abstractNote = {As power delivery systems evolve and become increasingly reliant on accurate forecasts, they will be more and more vulnerable to cybersecurity issues. A coordinated data attack by sophisticated adversaries can render existing data corrupt or outlier detection methods ineffective. This would have a very negative impact on operational decisions. The focus of this paper is to develop descriptive analytics-based methods for anomaly detection to protect the load forecasting process against cyberattacks to essential data. We propose an integrated solution (IS) and a hybrid implementation of IS (HIIS) that can detect and mitigate cyberattack induced long sequence anomalies. HIIS is also capable of improving true positive rates and reducing false positive rates significantly comparing with IS. The proposed HIIS can serve as an online cybersecure load forecasting scheme.},
doi = {10.1109/TSG.2019.2894334},
journal = {IEEE Transactions on Smart Grid},
number = 6,
volume = 10,
place = {United States},
year = {Fri Nov 01 00:00:00 EDT 2019},
month = {Fri Nov 01 00:00:00 EDT 2019}
}
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