Vulnerability Assessment of 6G-Enabled Smart Grid Cyber–Physical Systems
Abstract
Next-generation wireless communication and networking technologies, such as sixth-generation (6G) networks and software-defined Internet of Things (SDIoT), make cyber-physical systems (CPSs) more vulnerable to cyberattacks. In such massively connected CPSs, an intruder can trigger a cyberattack in the form of false data injection, which can lead to system instability. To address this issue, we propose a graphics-processing-unit-enabled adaptive robust state estimator. It comprises a deep learning algorithm, long short-term memory, and a nonlinear extended Kalman filter, and is called LSTMKF. Through an SDIoT controller, it provides an online parametric state estimate. The reliability is improved by performing two levels of online parametric state estimation for secure communication and load management. The CPS under study is a 6G and SDIoT-enabled smart grid, which is tested on IEEE 14, 30, and 118 bus systems. Compared to existing techniques, the proposed algorithm is able to estimate the state variables of the system even during or after a cyberattack, with lower time complexity and high accuracy.
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Given the growth of IoMT devices and large volumes of health traffic in modern smart health care systems, the centralized architecture results in scalability issues.
“Given the growth of IoMT devices and large volumes of health traffic in modern smart health care systems, the centralized architecture results in scalability issues [3].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [3]
Cites
6G Wireless Communications: Vision and Potential Techniques
Yang, P., Xiao, Y., Xiao, M., & Li, S. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800418 · Cited by 1,153 · see citing works
Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work
Zhao, J., Qi, J., Huang, Z., Meliopoulos, A. P. S., Gómez‐Expósito, A., Netto, M., Mili, L., Abur, A., Terzija, V. V., Kamwa, I., et al. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2894769 · Cited by 721 · see citing works
Blockchain for Future Smart Grid: A Comprehensive Survey
Mollah, M. B., Zhao, J., Niyato, D. T., Lam, K., Zhang, X., Ghias, A. M. Y. M., Koh, L. H., & Yang, L. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2993601 · Cited by 565 · see citing works
Probabilistic individual load forecasting using pinball loss guided LSTM
Wang, Y., Gan, D., Sun, M., et al. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.10.078 · Cited by 451 · see citing works
Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014
Makonin, S., Ellert, B., Bajić, I. V., & Popowich, F. P. (2016) · Scientific Data · doi:10.1038/sdata.2016.37 · Cited by 305 · see citing works
Forecasting-Aided State Estimation—Part I: Panorama
Filho, M. B. D. C., & de Souza, J. C. S. (2009) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2009.2030295 · Cited by 235 · see citing works
Intrusion Detection for Advanced Metering Infrastructures: Requirements and Architectural Directions
Berthier, R., Sanders, W. H., & Khurana, H. (2010) · IEEE International Conference on Smart Grid Communications (SmartGridComm) · doi:10.1109/smartgrid.2010.5622068 · Cited by 215 · see citing works
SCPSE: Security-Oriented Cyber-Physical State Estimation for Power Grid Critical Infrastructures
Zonouz, S., Rogers, K. M., Berthier, R., et al. (2012) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2012.2217762 · Cited by 179 · see citing works
Robust Routing Optimization for Smart Grids Considering Cyber-Physical Interdependence
Xu, L., Guo, Q., Yang, T., et al. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2888629 · Cited by 107 · see citing works
Load Forecasting Through Estimated Parametrized Based Fuzzy Inference System in Smart Grids
Ali, M., Adnan, M., Tariq, M., et al. (2020) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.2986982 · Cited by 106 · see citing works
Self-citationLoad-Balanced and QoS-Aware Software-Defined Internet of Things
Montazerolghaem, A., & Yaghmaee, M. H. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2967081 · Cited by 103 · see citing works
Bayesian linear state estimation using smart meters and PMUs measurements in distribution grids
Luca Schenato, B., Barchi, G., Macii, D., Arghandeh, R., Poolla, K. R., & von Meier, A. (2014) · IEEE International Conference on Smart Grid Communications (SmartGridComm) · doi:10.1109/smartgridcomm.2014.7007708 · Cited by 96 · see citing works
Opportunistic Routing for Smart Grid With Power Line Communication Access Networks
Yoon, S., Jang, S., Kim, Y., & Bahk, S. (2013) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2013.2279184 · Cited by 87 · see citing works
State Estimation in Power Distribution Systems Based on Ensemble Kalman Filtering
Carquex, C., Rosenberg, C., & Bhattacharya, K. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2847289 · Cited by 85 · see citing works
Evaluation of AMI and SCADA Data Synergy for Distribution Feeder Modeling
Huang, S., Lu, C., & Lo, Y.-L. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2408111 · Cited by 84 · see citing works
SDN-Enabled Energy-Efficient Routing Optimization Framework for Industrial Internet of Things
Naeem, F., Tariq, M., & Poor, H. V. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3006885 · Cited by 83 · see citing works
Self-citationNaeem, F., Srivastava, G., & Tariq, M. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.2991106 · Cited by 62 · see citing works
Self-citationOptimum control strategies for short term load forecasting in smart grids
Ali, M., Adnan, M., & Tariq, M. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.06.010 · Cited by 60 · see citing works
Self-citationConstrained Optimization of Multicast Routing for Wide Area Control of Smart Grid
Li, X., Tian, Y., Ledwich, G. F., et al. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2835487 · Cited by 47 · see citing works
Deployment of real-time state estimator and load flow in BC Hydro DMS - challenges and opportunities
Atanackovic, D., & Dabic, V. (2013) · IEEE Power & Energy Society General Meeting (PESGM) · doi:10.1109/pesmg.2013.6672408 · Cited by 36 · see citing works
Cyber‐physical robust control framework for enhancing transient stability of smart grids
Ayar, M., Trevizan, R. D., Obuz, S., et al. (2017) · IET Cyber-Physical Systems Theory & Applications · doi:10.1049/iet-cps.2017.0017 · Cited by 25 · see citing works
Blockchain-Enabled Reliable Osmotic Computing for Cloud of Things: Applications and Challenges
Rasool, S., Saleem, A., Iqbal, M., et al. (2020) · IEEE Internet of Things Magazine · doi:10.1109/iotm.0001.1900101 · Cited by 21 · see citing works
A Novel Approach to Fault Detection in Complex Electric Power Systems
Zhang, Y., & Wang, Z. (2014) · Advances in Electrical and Computer Engineering · doi:10.4316/aece.2014.03003 · Cited by 9 · see citing works
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Self-citationAli, M., Karimipour, H., & Tariq, M. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102355 · Cited by 177 · see citing works
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- Authors
- Tariq, M., Ali, M., Naeem, F., & Poor, H. V.
- Affiliations
- National University of Computer and Emerging Sciences (PK)
- Princeton University (US)
- Venue
- IEEE Internet of Things Journal
- Published
- 2021-04-01
- Volume
- 8
- Issue
- 7
- Pages
- 5468-5475
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3109531627
- Cited by
- 116
- References
- 23
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 37 · 2023: 15 · 2022: 33 · 2021: 11
- FWCI
- 7.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- U.S. National Science Foundation (CCF-1908308, ECCS-1824710)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
- Keywords
- smart grid, cyber-physical systems, 6G, software-defined Internet of Things, false data injection attacks, long short-term memory, load management, attack resilience, vulnerability assessment
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