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Future wireless networks (5G and beyond) are the vision of forthcomingcellular systems, connecting billions of devices and people together. In thelast decades, cellular networks have been dramatically growth with advancedtelecommunication technologies for high-speed data transmission, high cellcapacity, and low latency. The main goal of those technologies is to support awide 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 isto meet the high demand for those applications by improving and optimizingnetwork functions. Artificial Intelligence (AI) has a high potential to achievethese requirements by being integrated in applications throughout all layers ofthe network. However, the security concerns on network functions of NextG usingAI-based models, i.e., model poising, have not been investigated deeply.Therefore, it needs to design efficient mitigation techniques and securesolutions for NextG networks using AI-based methods. This paper proposes acomprehensive vulnerability analysis of deep learning (DL)-based channelestimation models trained with the dataset obtained from MATLAB's 5G toolboxfor adversarial attacks and defensive distillation-based mitigation methods.The adversarial attacks produce faulty results by manipulating trained DL-basedmodels for channel estimation in NextG networks, while making models morerobust against any attacks through mitigation methods. This paper also presentsthe performance of the proposed defensive distillation mitigation method foreach adversarial attack against the channel estimation model. The resultsindicated that the proposed mitigation method can defend the DL-based channelestimation models against adversarial attacks in NextG networks.

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