Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementSemantic SegmentationSimilar Papers 제목 키워드 기반
Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks
Advancements in wireless and mobile technologies, including 5G advanced and the envisioned 6G, are driving exponential growth in wireless devices. However, this rapid expansion exacerbates spectrum scarcity, posing a cri…
Data PoisoningFederated LearningWhen Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing
Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsified versions of spectrum sensing data to a fusion center. However, existing studies …
BIG-bench Machine LearningA Vision for Multisensory Intelligence: Sensing, Science, and Synergy
Our experience of the world is multisensory, spanning a synthesis of language, sight, sound, touch, taste, and smell. Yet, artificial intelligence has primarily advanced in digital modalities like text, vision, and audio…
Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks
An adversarial deep learning approach is presented to launch over-the-air spectrum poisoning attacks. A transmitter applies deep learning on its spectrum sensing results to predict idle time slots for data transmission. …
Deep LearningUpper Mid-Band Spectrum for 6G: Vision, Opportunity and Challenges
Driven by the pursuit of gigabit-per-second data speeds for future 6G mobile networks, in addition to the support of sensing and artificial intelligence applications, the industry is expanding beyond crowded sub-6 GHz ba…
Astronomy