  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.

Networking and Internet Architecture;Artificial Intelligence;Cryptography and Security