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Security and Privacy Challenges for AI in Wireless Communication

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1224

Special Issue Editors


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Guest Editor
College of Science and Engineering, Flinders University, Adelaide, GPO Box 2100, Adelaide 5001, Australia
Interests: physical layer security; wireless vital sign sensing; security and privacy; wireless communication; satellite communication

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Guest Editor
School of Management, Massey University, Private Bag 11 222, Palmerston North 4442, New Zealand
Interests: big data; data mining; data clustering

Special Issue Information

Dear Colleagues,

Over the past two decades, wireless communication has revolutionized modern life. From powerful mobile devices to tiny sensors transmitting data, it has impacted diverse applications, from consumer electronics to critical health and infrastructure devices. The recent rise of AI and its applications holds great potential for further transformation.

Similarly, wireless communication is not immune to AI’s influence. AI has been explored for waveform generation, resource allocation, power conservation, channel estimation, and RF parameter optimization. However, as with many technologies, security is often an afterthought, and the same applies to AI in wireless communication. Recent cyberattacks have prompted increased scrutiny of AI’s role in this field.

The main aim of this Special Issue is to address the security and privacy aspects of novel machine learning techniques for future wireless communication networks. Topics of interest include, but are not limited to, the following:

  • Threat analysis of AI in wireless communication;
  • Adversarial attacks on AI algorithms in wireless communication;
  • Adversarial machine learning for wireless communication systems;
  • AI attacks on wireless communication systems;
  • Mitigation techniques for the security of AI in wireless communication;
  • Security-aware techniques for AI in wireless communication.

Dr. Saeed Rehman
Dr. Shafiq Alam
Guest Editors

Manuscript Submission Information

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Keywords

  • adversarial machine learning
  • machine learning security
  • wireless attacks
  • security-aware AI
  • AI in wireless communication
  • AI defence
  • adversarial attacks
  • threat analysis
  • privacy in AI

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Published Papers (1 paper)

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Research

18 pages, 4723 KB  
Article
A Method for Specific Emitter Identification Based on Polarimetric Domain Feature Learning and Extraction
by Zixuan Zhang, Zhiyuan Ma, Zisen Qi, Jia Liang and Hua Xu
Sensors 2026, 26(8), 2368; https://doi.org/10.3390/s26082368 - 11 Apr 2026
Viewed by 482
Abstract
Specific Emitter Identification (SEI) distinguishes individual emitters by extracting subtle features from intercepted radio frequency signals. This process relies on the design and extraction of specific features. Current methods for selecting and characterizing radio frequency fingerprints vary by individual, and the extraction process [...] Read more.
Specific Emitter Identification (SEI) distinguishes individual emitters by extracting subtle features from intercepted radio frequency signals. This process relies on the design and extraction of specific features. Current methods for selecting and characterizing radio frequency fingerprints vary by individual, and the extraction process is closely coupled with environmental conditions. As a result, the generality of such identification algorithms is often limited, particularly when the application environment does not match the premise of feature design, leading to rapid degradation or even failure of individual identification performance. This paper proposes a deep clustering model based on polarization feature learning for identifying individual communication emitters. The approach involves constructing a guided network to extract datasets of polarization features from communication signals and utilizing a contrastive representation learning network to extract dual-polarization features from I/Q data samples. Subsequently, a Bayesian nonparametric (BNP) class mixture model algorithm, capable of inferring an unknown number of clusters, is employed to build a multi-level clustering network for clustering analysis of the extracted features. Under 5 dB conditions, the method described in this paper achieves an average recognition accuracy of 87.5%. Full article
(This article belongs to the Special Issue Security and Privacy Challenges for AI in Wireless Communication)
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