FGGM: Formal Grey-box Gradient Method for Attacking DRL-based MU-MIMO Scheduler

TL;DR

This paper introduces FGGM, a novel grey-box attack method exploiting DRL-based MU-MIMO schedulers in 5G, demonstrating significant throughput degradation by manipulating CSI inputs without exact victim observations.

Contribution

The paper proposes FGGM, a grey-box attack leveraging polytope abstract domains to effectively degrade network throughput in DRL-based MU-MIMO scheduling.

Findings

FGGM can reduce throughput by up to 70% in simulations.

Adversarial users can estimate CSI bounds using the observation normalizer.

The method is applicable to other DRL-based resource allocation problems.

Abstract

In 5G mobile communication systems, MU-MIMO has been applied to enhance spectral efficiency and support high data rates. To maximize spectral efficiency while providing fairness among users, the base station (BS) needs to selects a subset of users for data transmission. Given that this problem is NP-hard, DRL-based methods have been proposed to infer the near-optimal solutions in real-time, yet this approach has an intrinsic security problem. This paper investigates how a group of adversarial users can exploit unsanitized raw CSIs to launch a throughput degradation attack. Most existing studies only focused on systems in which adversarial users can obtain the exact values of victims' CSIs, but this is impractical in the case of uplink transmission in LTE/5G mobile systems. We note that the DRL policy contains an observation normalizer which has the mean and variance of the observation to improve training convergence. Adversarial users can then estimate the upper and lower bounds of the local observations including the CSIs of victims based solely on that observation normalizer. We develop an attacking scheme FGGM by leveraging polytope abstract domains, a technique used to bound the outputs of a neural network given the input ranges. Our goal is to find one set of intentionally manipulated CSIs which can achieve the attacking goals for the whole range of local observations of victims. Experimental results demonstrate that FGGM can determine a set of adversarial CSI vector controlled by adversarial users, then reuse those CSIs throughout the simulation to reduce the network throughput of a victim up to 70\% without knowing the exact value of victims' local observations. This study serves as a case study and can be applied to many other DRL-based problems, such as a knapsack-oriented resource allocation problems.