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SINR-Based DoS Attack on Remote State Estimation: A Game-Theoretic Approach
Open accessAbstract
We consider remote state estimation of cyberphysical systems under signal-to-interference-plus-noise ratio-based denial-of-service attacks. A sensor sends its local estimate to a remote estimator through a wireless network that may suffer interference from an attacker. Both the sensor and the attacker have energy constraints. We first study an associated two-player game when multiple power levels are available. Then, we build a Markov game framework to model the interactive decision-making process based on the current state and information collected from previous time steps. To solve the associated optimality (Bellman) equations, a modified Nash Q-learning algorithm is applied to obtain the optimal solutions. Numerical examples and simulations are provided to demonstrate our results.
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Xiao, L., Wan, X., Lu, X., Zhang, Y., & Wu, D. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2018.2825478 · Cited by 681
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
State of the art of cyber-physical systems security: An automatic control perspective
Zacchia Lun, Y., D’Innocenzo, A., Smarra, F., Malavolta, I., & Di Benedetto, M. D. (2019) · Journal of Systems and Software · doi:10.1016/j.jss.2018.12.006 · Cited by 211
Machine Learning in IoT Security: Current Solutions and Future Challenges
Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986444 · Cited by 129
Metadata
- Authors
- Li, Y., Quevedo, D. E., Dey, S., & Shi, L.
- Venue
- IEEE Transactions on Control of Network Systems
- Published
- 2017-09
- Volume
- 4
- Issue
- 3
- Pages
- 632-642
- ISSN
- 2325-5870 (ISSN-L)
- OpenAlex
- W2324627801
- Cited by
- 355
- References
- 29
- FWCI
- 24.41
- Type
- journal article
- Language
- en
- Open access
- green
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