# When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

arXiv: https://arxiv.org/abs/2201.04677 · HTML: https://mlsanity.com/paper/2201.04677/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 167 references that are in the map.
- First posted on arXiv 12 Jan 2022.

## Builds on (most cited first, 30 of 35)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [Machine Learning with Membership Privacy using Adversarial Regularization](https://mlsanity.com/paper/1807.05852/) (2018)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Dynamic Backdoor Attacks Against Machine Learning Models](https://mlsanity.com/paper/2003.03675/) (2020)
- [AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning](https://mlsanity.com/paper/1805.04810/) (2018)
- [On Safeguarding Privacy and Security in the Framework of Federated Learning](https://mlsanity.com/paper/1909.06512/) (2019)
- [IoT Network Security from the Perspective of Adversarial Deep Learning](https://mlsanity.com/paper/1906.00076/) (2019)
- [Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks](https://mlsanity.com/paper/1911.00500/) (2019)
- [Adaptive Federated Learning With Gradient Compression in Uplink NOMA](https://mlsanity.com/paper/2003.01344/) (2020)
- [Spectrum Data Poisoning with Adversarial Deep Learning](https://mlsanity.com/paper/1901.09247/) (2019)
- [When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing](https://mlsanity.com/paper/1905.01430/) (2019)
- [Generative Adversarial Learning for Spectrum Sensing](https://mlsanity.com/paper/1804.00709/) (2018)
- ["Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications](https://mlsanity.com/paper/1904.03897/) (2019)
- [Byzantine Attack and Defense in Cognitive Radio Networks: A Survey](https://mlsanity.com/paper/1504.01185/) (2015)
- [Spectrum Sharing for Internet of Things: A Survey](https://mlsanity.com/paper/1810.04408/) (2018)
- [Transfer Learning for Device Fingerprinting with Application to Cognitive Radio Networks](https://mlsanity.com/paper/1508.06614/) (2015)
- [Ambient Backscatter Communications: A Contemporary Survey](https://mlsanity.com/paper/1712.04804/) (2017)
- [Robust Deep Sensing Through Transfer Learning in Cognitive Radio](https://mlsanity.com/paper/1908.00658/) (2019)
- [Private and Truthful Aggregative Game for Large-Scale Spectrum Sharing](https://mlsanity.com/paper/1608.05537/) (2016)
- [Spectrum Access In Cognitive Radio Using A Two Stage Reinforcement Learning Approach](https://mlsanity.com/paper/1707.09792/) (2017)
- [Subcarrier Assignment Schemes Based on Q-Learning in Wideband Cognitive Radio Networks](https://mlsanity.com/paper/1911.07149/) (2019)
- [Database-assisted Distributed Spectrum Sharing](https://mlsanity.com/paper/1301.2848/) (2013)
- [Explore Recurrent Neural Network for PUE Attack Detection in Practical CRN Models](https://mlsanity.com/paper/1805.00428/) (2018)
- [Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks](https://mlsanity.com/paper/1810.04520/) (2018)
- [Securing Cognitive Radio Networks against Primary User Emulation Attacks](https://mlsanity.com/paper/1308.6216/) (2013)
- [Location Privacy Preservation in Database-Driven Wireless Cognitive Networks Through Encrypted Probabilistic Data Structures](https://mlsanity.com/paper/1806.03557/) (2018)
- [Reinforcement Learning Approach for RF-Powered Cognitive Radio Network with Ambient Backscatter](https://mlsanity.com/paper/1808.07601/) (2018)
- [Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System with Online Reinforcement Learning](https://mlsanity.com/paper/1809.02753/) (2018)

## Built on by (most cited first, 6 of 6)

- [Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges](https://mlsanity.com/paper/2309.07438/) (2023)
- [Secure Federated Learning for Cognitive Radio Sensing](https://mlsanity.com/paper/2304.06519/) (2023)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Changepoint Detection for Real-Time Spectrum Sharing Radar](https://mlsanity.com/paper/2207.06409/) (2022)
- [The Future of Internet of Things and Multimodal Language Models in 6G Networks: Opportunities and Challenges](https://mlsanity.com/paper/2504.13971/) (2025)
- [LLM Assisted Alpha Fairness for 6 GHz WiFi and NR_U Coexistence: An Agentic Orchestrator for Throughput, Energy, and SLA](https://mlsanity.com/paper/2510.17814/) (2025)

## Abstract (arXiv)

The exponential growth of internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to different potential security and privacy issues, requiring protection mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based methods have frequently been proposed to address those issues. In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms. In particular, we elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects, including ML based cognitive radio networks (CRNs), ML based database assisted SS networks, ML based LTE-U networks, ML based ambient backscatter networks, and other ML based SS solutions. We also present security issues from the physical layer and corresponding defending strategies based on ML algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and privacy issues. Finally, extensive discussions on open challenges for ML based SS are also given. This comprehensive review is intended to provide the foundation for and facilitate future studies on exploring the potential of emerging ML for coping with increasingly complex SS and their security problems.