[2209.13007] Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals
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Computer Science > Networking and Internet Architecture

arXiv:2209.13007 (cs)
[Submitted on 27 Sep 2022]

Title:Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals

Authors:Ferhat Ozgur Catak, Murat Kuzlu, Salih Sarp, Evren Catak, Umit Cali
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Abstract:Cellular networks (LTE, 5G, and beyond) are dramatically growing with high demand from consumers and more promising than the other wireless networks with advanced telecommunication technologies. The main goal of these networks is to connect billions of devices, systems, and users with high-speed data transmission, high cell capacity, and low latency, as well as to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, advanced manufacturing, and many more. To achieve these goals, spectrum sensing has been paid more attention, along with new approaches using artificial intelligence (AI) methods for spectrum management in cellular networks. This paper provides a vulnerability analysis of spectrum sensing approaches using AI-based semantic segmentation models for identifying cellular network signals under adversarial attacks with and without defensive distillation methods. The results showed that mitigation methods can significantly reduce the vulnerabilities of AI-based spectrum sensing models against adversarial attacks.
Comments: IEEE GLOBECOM 2022 Publication
Subjects: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
Cite as: arXiv:2209.13007 [cs.NI]
  (or arXiv:2209.13007v1 [cs.NI] for this version)
  https://doi.org/10.48550/arXiv.2209.13007

Submission history

From: Ferhat Ozgur Catak [view email]
[v1] Tue, 27 Sep 2022 11:14:47 UTC (5,128 KB)
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