Backdoor Attacks Against Deep Learning-Based Massive MIMO Localization

Tianya Zhao, Xuyu Wang, Shiwen Mao

Abstract: Millimeter wave (mmWave) communications and massive MIMO play crucial roles in the development of future wireless systems. In addition to offering high data rates, these technologies enable the realization of high-precision localization systems, especially in complicated indoor rich multi-path environments without GPS coverage. While deep neural networks (DNNs) enable high accuracy in fingerprint-based indoor localization, their implementations also introduce security problems. In the field of computer vision, backdoor attacks have proven to be able to effectively deceive models using specific or imperceptible triggers. In this paper, we study the impact of backdoor attacks on 5G massive MIMO localization systems in both indoor and outdoor environments. Two different triggers are investigated: the one-pixel trigger (visible) and the random noise trigger (invisible). We evaluate the localization systems using a public dataset and demonstrate that DNN-based localization systems are vulnerable to backdoor attacks.