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

TL;DR

This paper analyzes vulnerabilities of AI-based spectrum sensing in cellular networks and demonstrates that mitigation techniques can significantly enhance robustness against adversarial attacks.

Contribution

It provides a vulnerability analysis of AI spectrum sensing models and evaluates mitigation strategies to improve their security in cellular networks.

Findings

Mitigation methods reduce vulnerabilities of AI spectrum sensing models.

Adversarial attacks can significantly compromise spectrum sensing accuracy.

Defensive distillation enhances model robustness against attacks.

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.