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Cellular networks (LTE, 5G, and beyond) are dramatically growing with highdemand from consumers and more promising than the other wireless networks withadvanced telecommunication technologies. The main goal of these networks is toconnect billions of devices, systems, and users with high-speed datatransmission, high cell capacity, and low latency, as well as to support a widerange of new applications, such as virtual reality, metaverse, telehealth,online education, autonomous and flying vehicles, advanced manufacturing, andmany more. To achieve these goals, spectrum sensing has been paid moreattention, along with new approaches using artificial intelligence (AI) methodsfor spectrum management in cellular networks. This paper provides avulnerability analysis of spectrum sensing approaches using AI-based semanticsegmentation models for identifying cellular network signals under adversarialattacks with and without defensive distillation methods. The results showedthat mitigation methods can significantly reduce the vulnerabilities ofAI-based spectrum sensing models against adversarial attacks.

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