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Deep Learning for Signal Authentication and Security in Massive Internet-of-Things Systems
Open accessAbstract
Secure signal authentication is arguably one of the most challenging problems in the Internet of Things (IoT), due to the large-scale nature of the system and its susceptibility to man-in-the-middle and data-injection attacks. In this paper, a novel watermarking algorithm is proposed for dynamic authentication of IoT signals to detect cyber-attacks. The proposed watermarking algorithm, based on a deep learning long short-term memory structure, enables the IoT devices (IoTDs) to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the IoT gateway, which collects signals from the IoTDs, to effectively authenticate the reliability of the signals. Moreover, in massive IoT scenarios, since the gateway cannot authenticate all of the IoTDs simultaneously due to computational limitations, a game-theoretic framework is proposed to improve the gateway's decision making process by predicting vulnerable IoTDs. The mixed-strategy Nash equilibrium (MSNE) for this game is derived, and the uniqueness of the expected utility at the equilibrium is proven. In the massive IoT system, due to the large set of available actions for the gateway, the MSNE is shown to be analytically challenging to derive, and thus, a learning algorithm that converges to the MSNE is proposed. Moreover, in order to handle incomplete information scenarios, in which the gateway cannot access the state of the unauthenticated IoTDs, a deep reinforcement learning algorithm is proposed to dynamically predict the state of unauthenticated IoTDs and allow the gateway to decide on which IoTDs to authenticate. Simulation results show that with an attack detection delay of under 1 s, the messages can be transmitted from IoTDs with an almost 100% reliability. The results also show that by optimally predicting the set of vulnerable IoTDs, the proposed deep reinforcement learning algorithm reduces the number of compromised IoTDs by up to 30%, compared to an equal probability baseline.
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“In addition, deep RL can be used for signal authentication [149].”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [149]
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Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
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Xu, L. D., He, W., & Li, S. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2300753 · Cited by 5,172
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Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. A. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2926625 · Cited by 1,378
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Stergiou, C. L., Psannis, K. E., Kim, B., & Gupta, B. B. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.11.031 · Cited by 1,114
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Metadata
- Authors
- Ferdowsi, A., & Saad, W.
- Affiliations
- Virginia Tech (US)
- Venue
- IEEE Transactions on Communications
- Published
- 2019-02
- Volume
- 67
- Issue
- 2
- Pages
- 1371-1387
- ISSN
- 0090-6778 (ISSN-L)
- 1558-0857
- OpenAlex
- W2793350679
- Cited by
- 160
- References
- 50
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 18 · 2023: 19 · 2022: 22 · 2021: 30 · 2020: 28 · 2019: 20
- FWCI
- 12.70
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (OAC-1541105, CNS-1446621, CNS-CNS-1524634)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Keywords
- signal authentication, Internet of Things security, dynamic watermarking, long short-term memory, cyberattack detection, man-in-the-middle attacks, false data injection attacks, mixed strategy equilibrium, deep reinforcement learning, IoT gateway, incomplete information games
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