Hai Duong

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.

Safety-critical applications like autonomous vehicles and industrial IoT are adopting semantic communication (SemCom) systems using deep neural networks to reduce bandwidth and increase transmission speed by transmitting only task-relevant semantic features. However, adversarial attacks against these DNN-based SemCom systems can cause catastrophic failures by manipulating transmitted semantic features. Existing defense mechanisms rely on empirical approaches provide no formal guarantees against the full spectrum of adversarial perturbations. We present VSCAN, a neural network verification framework that provides mathematical robustness guarantees by formulating adversarial noise generation as mixed integer programming and verifying end-to-end properties across multiple interconnected networks (encoder, decoder, and task model). Our key insight is that realistic adversarial constraints (power limitations and statistical undetectability) can be encoded as logical formulae to enable efficient verification using state-of-the-art DNN verifiers. Our evaluation on 600 verification properties characterizing various attacker's capabilities shows VSCAN matches attack methods in finding vulnerabilities while providing formal robustness guarantees for 44% of properties -- a significant achievement given the complexity of multi-network verification. Moreover, we reveal a fundamental security-efficiency tradeoff: compact 16-dimensional latent spaces achieve 50% verified robustness compared to 64-dimensional spaces.