A Counter-Eavesdropping Technique for Optimized Privacy of Wireless Industrial IoT Communications
Open accessAbstract
The industrial Internet of Things (IIoTs) is a key component of the fourth industrial revolution (Industry 4.0) which is faced with privacy issues as the scale and sensitivity of user and system data constantly increases. Eavesdropping attack is one of such privacy issue of the IIoT system especially when the number of transmitting antennas is increased. Thus, the focus of this article is on establishing efficient privacy in an IIoT-multiple-input–multiple-output–multiple-antenna eavesdropping communications scenario. To achieve this, a closed-form derivation for asymptotic regularized prompt privacy rate is first formulated for IIoT network system. Then, the study further examines the design of optimal jamming parameters by proposing a model referred as optimal counter-eavesdropping channel approximation technique for tackling eavesdropping attack in IIoT. The simulated performance of the proposed model clearly shows that provided that the channel coherence time is less than two times the number of transmitting nodes, a high privacy precision is achieved even without deploying any artificial noise.
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Under a wireless configuration, an attacker is likely to eavesdrop on the local model updates of other benign clients by exploiting the broadcast nature of radio.
“Under a wireless configuration, an attacker is likely to eavesdrop on the local model updates of the other benign clients by exploiting the broadcast nature of radio [173].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [173]
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Cites
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“Under a wireless configuration, an attacker is likely to eavesdrop on the local model updates of the other benign clients by exploiting the broadcast nature of radio [173].”
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“By proactively incorporating these countermeasures, organizations can bolster the security of their IIoT network layer and safeguard against eavesdropping threats [81], [82], [83].”
Background
- Authors
- Anajemba, J. H., Iwendi, C., Razzak, I., Ansere, J. A., & Okpalaoguchi, I. M.
- Affiliations
- Department of ML and AI, Wuhan Bofa Technology Company, Ltd., Wuhan, China
- University of Greater Manchester (GB)
- Deakin University (AU)
- Sunyani Technical University (GH)
- CIO Logistics, Kearny, NJ, USA
- ORCID
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2022-09
- Volume
- 18
- Issue
- 9
- Pages
- 6445-6454
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4206822447
- Cited by
- 47
- References
- 20
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 17 · 2023: 9 · 2022: 5
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- green
- ub-ir.bolton.ac.uk
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- UBIR (University of Greater Manchester) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Wireless Communication Security Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Keywords
- eavesdropping, multiple-antenna communication, artificial noise, privacy-utility trade-off, channel coherence time
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