The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Detection of power grid disturbances and cyber-attacks based on machine learning
Abstract
Abstract Modern intelligent power grid provides an efficient way of managing energy supply and consumption while facing numerous security threats at the same time. Both natural and man-made events can cause power system disturbance. Therefore, it is important for operators to identify the specific causes and types of disturbance in the power system to make decisions and respond appropriately. In order to address this problem, this paper proposes an attack detection model for power system based on machine learning that can be trained by using information and logs collected by phasor measurement units (PMUs). We carry out feature construction engineering, and then send the data to different machine learning models, in which random forest is chosen as the basic classifier of AdaBoost. The model is evaluated using open-source simulated power system data, which consists of 37 power system event scenarios. Finally, we compare the proposed model with other models by using different evaluation metrics. As the experimental results demonstrate that this model can achieve accuracy rate of 93.91% and detection rate of 93.6%, higher than eight recently developed techniques.
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Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · arXiv · doi:10.48550/arxiv.1603.02754 · Cited by 7,104
Feature Selection: A Data Perspective
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2018) · ACM Computing Surveys · doi:10.1145/3136625 · Cited by 2,442
A survey of physics-based attack detection in cyber-physical systems
Giraldo, J., Urbina, D., Cardenas, A., Valente, J., Faisal, M., Ruths, J., Tippenhauer, N. O., Sandberg, H., & Candell, R. (2019) · ACM Computing Surveys · doi:10.1145/3203245 · Cited by 535
A Taxonomy of Cyber Attacks on SCADA Systems
Zhu, B., Joseph, A., & Sastry, S. (2011) · 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing · doi:10.1109/ithings/cpscom.2011.34 · Cited by 458
Pan, S., Morris, T., & Adhikari, U. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2420951 · Cited by 180
${l_p}$-norms in One-Class Classification for Intrusion Detection in SCADA Systems
Nader, P., Honeine, P., & Beauseroy, P. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2330796 · Cited by 122
Cited by
Machlev, R., Heistrene, L., Perl, M., Levy, K. Y., Belikov, J., Mannor, S., & Levron, Y. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100169 · Cited by 552
Machine learning driven smart electric power systems: Current trends and new perspectives
Ibrahim, M. S., Dong, W., & Yang, Q. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115237 · Cited by 435
Abdullahi, M., Baashar, Y., Alhussian, H., Alwadain, A., Aziz, N., Capretz, L. F., & Abdulkadir, S. J. (2022) · Electronics · doi:10.3390/electronics11020198 · Cited by 361
Machine learning and data analytics for the IoT
Adi, E., Anwar, A., Baig, Z., & Zeadally, S. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04874-y · Cited by 279
Robles-Durazno, A., Moradpoor, N., McWhinnie, J., Russell, G., & Tan, Z. (2021) · Ad Hoc Networks · doi:10.1016/j.adhoc.2021.102590 · Cited by 13
Metadata
- Authors
- Wang, D., Wang, X., Zhang, Y., & Jin, L.
- Venue
- Journal of Information Security and Applications
- Publisher
- Elsevier BV
- Published
- 2019-06
- Volume
- 46
- Pages
- 42-52
- ISSN
- 2214-2126 (ISSN-L)
- OpenAlex
- W2919785014
- Cited by
- 207
- References
- 6
- FWCI
- 14.71
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, D., Wang, X., Zhang, Y., & Jin, L. (2019). Detection of power grid disturbances and cyber-attacks based on machine learning. Journal of Information Security and Applications. https://doi.org/10.1016/j.jisa.2019.02.008
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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