# Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks

arXiv: https://arxiv.org/abs/2208.10279 · HTML: https://mlsanity.com/paper/2208.10279/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 18 of 18)

- [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)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [A Very Brief Introduction to Machine Learning With Applications to Communication Systems](https://mlsanity.com/paper/1808.02342/) (2018)
- [ML Attack Models: Adversarial Attacks and Data Poisoning Attacks](https://mlsanity.com/paper/2112.02797/) (2021)
- [Adversarial Attacks and Defenses for Speech Recognition Systems](https://mlsanity.com/paper/2103.17122/) (2021)
- [Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples](https://mlsanity.com/paper/2102.04150/) (2021)
- [On the Vulnerability of Capsule Networks to Adversarial Attacks](https://mlsanity.com/paper/1906.03612/) (2019)
- [Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures](https://mlsanity.com/paper/2201.03866/) (2022)
- [Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges](https://mlsanity.com/paper/2001.08159/) (2020)
- [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)
- [Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks](https://mlsanity.com/paper/2005.08281/) (2020)
- [Learning-Driven Wireless Communications, towards 6G](https://mlsanity.com/paper/1908.07335/) (2019)
- [Computing Research Challenges in Next Generation Wireless Networking](https://mlsanity.com/paper/2101.01279/) (2021)

## Built on by (most cited first, 2 of 2)

- [Knowledge Distillation for mmWave Beam Prediction Using Sub-6 GHz Channels](https://mlsanity.com/paper/2602.04703/) (2026)
- [Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation](https://mlsanity.com/paper/2404.03088/) (2024)

## Abstract (arXiv)

Future wireless networks (5G and beyond) are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have been dramatically growth with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated in applications throughout all layers of the network. However, the security concerns on network functions of NextG using AI-based models, i.e., model poising, have not been investigated deeply. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB's 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks, while making models more robust against any attacks through mitigation methods. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack against the channel estimation model. The results indicated that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks.