The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids
Open accessAbstract
Smart grid technology increases reliability, security, and efficiency of the electrical grids. However, its strong dependencies on digital communication technology bring up new vulnerabilities that need to be considered for efficient and reliable power distribution. In this paper, an unsupervised anomaly detection based on statistical correlation between measurements is proposed. The goal is to design a scalable anomaly detection engine suitable for large-scale smart grids, which can differentiate an actual fault from a disturbance and an intelligent cyber-attack. The proposed method applies feature extraction utilizing symbolic dynamic filtering (SDF) to reduce computational burden while discovering causal interactions between the subsystems. The simulation results on IEEE 39, 118, and 2848 bus systems verify the performance of the proposed method under different operation conditions. The results show an accuracy of 99%, true positive rate of 98%, and false positive rate of less than 2%
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A survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480
Self-citationApplications of artificial intelligence and machine learning in smart cities
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.069 · Cited by 756
Ghiasi, M., Niknam, T., Wang, Z., Mehrandezh, M., Dehghani, M., & Ghadimi, N. (2023) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108975 · Cited by 494
Machine learning based solutions for security of Internet of Things (IoT): A survey
Tahsien, S. M., Karimipour, H., & Spachos, P. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102630 · Cited by 470
Self-citationMachine 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
Metadata
- Authors
- Karimipour, H., Dehghantanha, A., Parizi, R. M., Choo, K.-K. R., & Leung, H.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 80778-80788
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2946893466
- Cited by
- 355
- References
- 36
- FWCI
- 23.31
- Type
- journal article
- Language
- en
- Open access
- gold
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- Karimipour, H., Dehghantanha, A., Parizi, R. M., Choo, K.-K. R., & Leung, H. (2019). A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids. IEEE Access. https://doi.org/10.1109/access.2019.2920326
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