The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Digital Twin for Wireless Networks: Security Attacks and Solutions
Abstract
Emerging applications (e.g., human-computer interaction, digital healthcare, and intelligent transportation systems) can be effectively enabled using digital twins (DTs) that combine the virtual model of a physical system with other technologies/ schemes (i.e., mathematical optimization, game theory, and machine learning). Although DTs can well enable wireless systems, they will suffer from various security attacks. Therefore, this article presents an overview of security attacks and concerns in a DT-enabled wireless system. We propose a general framework that will serve as guidelines for future DT-enabled wireless systems. Additionally, we present two use cases to show the effectiveness of the proposed framework. A Stackelberg game-enabled model is provided to effectively enable anti-jamming attacks. In addition a residual- enabled reweighting aggregation scheme furnishes robust training against flawed parameters. Finally, we present an outlook on future directions.
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In wireless networks, DTs are susceptible to attacks, requiring security solutions such as Stackelberg game-based models for anti-jamming and residual-enabled reweighting aggregation methods.
“In wireless networks, DTs are susceptible to attacks, requiring security solutions such as Stackelberg game-based models for anti-jamming and residual-enabled reweighting aggregation methods to improve training resilience against incorrect parameters [114].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [114]
Citations
Cites
Digital Twin in Industry: State-of-the-Art
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391
Digital twin: Enabling technologies, challenges and open research
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020) · IEEE Access · doi:10.1109/access.2020.2998358 · Cited by 2,965
Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions
Khan, L. U., Saad, W., Niyato, D. T., Han, Z., & Hong, C. S. (2022) · IEEE Communications Magazine · doi:10.1109/mcom.001.21143 · Cited by 559
Self-citationDigital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities
Khan, L. U., Han, Z., Saad, W., Hossain, E., Guizani, M., & Hong, C. S. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3198273 · Cited by 340
Self-citationDynamic Digital Twin and Federated Learning With Incentives for Air-Ground Networks
Sun, W. Y., Xu, N., Wang, L., Zhang, H., & Zhang, Y. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3048137 · Cited by 174
A deep learning approach for effective intrusion detection in wireless networks using CNN
Riyaz, B., & Ganapathy, S. (2020) · Soft Computing · doi:10.1007/s00500-020-05017-0 · Cited by 171
BSIF: Blockchain-Based Secure, Interactive, and Fair Mobile Crowdsensing
Wang, W., Yang, Y., Yin, Z., et al. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3213306 · Cited by 125
Self-citationState-of-the-Art Review on IoT Threats and Attacks: Taxonomy, Challenges and Solutions
Krishna, R. R., Priyadarshini, A., Jha, A. V., Appasani, B., Srinivasulu, A., & Bizon, N. (2021) · Sustainability · doi:10.3390/su13169463 · Cited by 112
Attack-Resistant Federated Learning with Residual-based ReweightingPreprint
Fu, S., Xie, C., Li, B., & Chen, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11464 · Cited by 91
Yang, Y., Wang, W., Liu, L., et al. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3157394 · Cited by 77
Self-citationMetaverse for Wireless Systems: Vision, Enablers, Architecture, and Future Directions
Khan, L. U., Han, Z., Niyato, D. T., Guizani, M., & Hong, C. S. (2024) · IEEE Wireless Communications · doi:10.1109/mwc.013.2300287 · Cited by 68
Self-citationMetaverse for wireless systems: Architecture, advances, standardization, and open challenges
Khan, L. U., Guizani, M., Niyato, D. T., Al‐Fuqaha, A., & Debbah, M. A. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101121 · Cited by 55
Self-citationSakhnini, J., Karimipour, H., Dehghantanha, A., et al. (2021) · Physical Communication · doi:10.1016/j.phycom.2021.101394 · Cited by 49
IoT Network Attack Detection and Mitigation
Gelenbe, E., Fröhlich, P., Nowak, M. P., et al. (2020) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco49872.2020.9134241 · Cited by 42
Security Attacks and Solutions for Digital TwinsPreprint
Suhail, S., Jurdak, R., & Hussain, R. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.12501 · Cited by 19
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Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0
Mon, B. F., Hayajneh, M., Ali, N. A., Ullah, F., Ullah, H., & Alkobaisi, S. (2025) · Computers, Materials & Continua · doi:10.32604/cmc.2025.061411 · Cited by 26
“In wireless networks, DTs are susceptible to attacks, requiring security solutions such as Stackelberg game-based models for anti-jamming and residual-enabled reweighting aggregation methods to improve training resilience against incorrect parameters [114].”
Metadata
- Authors
- Wang, W., Yang, Y., Khan, L. U., Niyato, D., Han, Z., & Guizani, M.
- Affiliations
- City University of Hong Kong (HK)
- PLA Army Engineering University (CN)
- Mohamed bin Zayed University of Artificial Intelligence (AE)
- Nanyang Technological University (SG)
- University of Houston,USA
- Venue
- IEEE Wireless Communications
- Published
- 2024-06
- Volume
- 31
- Issue
- 3
- Pages
- 278-285
- ISSN
- 1536-1284 (ISSN-L)
- 1558-0687
- OpenAlex
- W4388819599
- Cited by
- 30
- References
- 15
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 6
- FWCI
- 3.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Wireless Communications (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Wireless Communication Security Techniques
- Keywords
- digital twin, wireless networks, security attacks, anti-jamming, Stackelberg game, robust training
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