Intelligent networking in adversarial environment: challenges and opportunities

Abstract

Although deep learning technologies have been widely exploited in many fields, they are vulnerable to adversarial attacks by adding small perturbations to legitimate inputs to fool targeted models. However, few studies have focused on intelligent networking in such an adversarial environment, which can pose serious security threats. In fact, while challenging intelligent networking, adversarial environments also bring about opportunities. In this paper, we, for the first time, simultaneously analyze the challenges and opportunities that the adversarial environment brings to intelligent networking. Specifically, we focus on challenges that the adversarial environment will pose on the existing intelligent networking. Furthermore, we investigate frameworks and approaches that combine adversarial machine learning with intelligent networking to solve the existing deficiencies of intelligent networking. Finally, we summarize the issues, including opportunities and challenges, which can allow researchers to focus on intelligent networking in adversarial environments.

AI-Powered 5G Networks: A Roadmap for The Future of Wireless Communications

“…The paper discusses the ways in which AI could be employed to improve the performance, security, and user experience of 5G networks. Further, Li et al[19] in (2022) presented his study in AI in 5G Networks discussing the challenges and opportunities for implementing AI in 5G networks. The paper discusses the technical challenges of implementing AI in 5G networks, as well as the regulatory challenges and the social implications of using AI in 5G networks.…”