Evaluating Sensitivity and Stability in Secure Deep Learning Based Channel Estimation for NextG Networks with Defensive Distillation

In the domain of wireless networks, including 5G and beyond, envisions a highly interconnected environment, enabling swift data transmission, expanded cell capacity, and minimal latency to support various applications and advanced manufacturing. Next Generation (NextG) networks seek to optimize and enhance network functionalities through Artificial intelligence (AI) integration, although concerns persist regarding the security of AI based models, particularly regarding model poisoning. Protecting these networks against cybersecurity threats, especially adversarial attacks, is vital. Hence, effective mitigation techniques and secure solutions employing AI-based methods are crucial. This study delves into the pivotal aspects of stability and sensitivity in AI-driven models tailored for NextG networks, focusing on Deep learning (DL) based channel estimation models. Utilizing data from MATLAB's 5G toolbox, we scrutinize these models' susceptibility to adversarial attacks and evaluate the efficacy of defensive distillation techniques in mitigating vulnerabilities. The research highlights the urgent necessity to fortify DL-based models against potential threats within NextG networks. Through a comprehensive evaluation of defensive distillation strategies, we showcase their ability to bolster both the stability and sensitivity of channel estimation models. By addressing these critical aspects, the study aims to enhance the resilience of AI-driven solutions within NextG environments, strengthening defenses against cybersecurity risks and safeguarding network integrity. Ultimately, the findings underscore the importance of prioritizing sensitivity and stability in the development and deployment of AI-based models for NextG networks. Through proactive measures and advanced mitigation methods, efforts are made to mitigate the risks posed by adversarial attacks and foster a more secure and resilient network infrastructure for the future.