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.
Bibliographical noteFunding Information:
This work was in part supported by National Science Foundation for Distinguished Young Scholars of China (Grant No. 61825204), National Natural Science Foundation of China (Grant Nos. 61932016, 62132011), Beijing Outstanding Young Scientist Program (Grant No. BJJWZYJH01201910003011), China Postdoctoral Science Foundation (Grant No. 2021M701894), and China National Postdoctoral Program for Innovative Talents. Dan WANG’s work is supported in part by General Research Fund (Grant Nos. 15210119, 15209220, 15200321), Innovation Technology Fund (ITSP Program ITS/070/19FP), Collaborative Research Fund (Grant Nos. C5026-18G, C5018-20G), The Hong Kong Polytechnic University (Grant No. 1-ZVPz), and a Huawei Collaborative Project. We also thank anonymous reviewers for their comments and guidance.
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- intelligent networking