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

arXiv: https://arxiv.org/abs/2209.13007 · HTML: https://mlsanity.com/paper/2209.13007/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [Adversarial Logit Pairing](https://mlsanity.com/paper/1803.06373/) (2018)
- [Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case](https://mlsanity.com/paper/2204.07752/) (2022)
- [Deep Learning for Spectrum Sensing](https://mlsanity.com/paper/1909.02730/) (2019)
- [Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case](https://mlsanity.com/paper/2103.07268/) (2021)
- [Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction](https://mlsanity.com/paper/2105.03905/) (2021)

## Abstract (arXiv)

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.