| Title | Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Manoj, B. R., Sadeghi, Meysam, Larsson, Erik G. |
| Conference Name | ICC 2021 - IEEE International Conference on Communications |
| Date Published | jun |
| Keywords | adversarial attacks, Artificial neural networks, Chained Attacks, Deep Learning, massive MIMO, Neural networks, Perturbation methods, power allocation, precoding, pubcrawl, Resiliency, Resource management, Scalability, Tools, Wireless communication, wireless security |
| Abstract | Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. However, for many classification tasks (e.g., modulation classification) it has been shown that DL-based wireless systems are susceptible to adversarial examples; adversarial examples are well-crafted malicious inputs to the neural network (NN) with the objective to cause erroneous outputs. In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. Specifically, we extend the fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent adversarial attacks in the context of power allocation in a maMIMO system. We benchmark the performance of these attacks and show that with a small perturbation in the input of the NN, the white-box attacks can result in infeasible solutions up to 86%. Furthermore, we investigate the performance of black-box attacks. All the evaluations conducted in this work are based on an open dataset and NN models, which are publicly available. |
| DOI | 10.1109/ICC42927.2021.9500424 |
| Citation Key | manoj_adversarial_2021 |
Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network