Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems

• URL: http://arxiv.org/abs/2311.00207v3
• Date: Thu, 21 Nov 2024 07:54:02 GMT
• Title: Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
• Authors: Jung-Woo Chang, Ke Sun, Nasimeh Heydaribeni, Seira Hidano, Xinyu Zhang, Farinaz Koushanfar,
• Abstract summary: Magmaw is a novel wireless attack methodology capable of generating universal adversarial perturbations for any multimodal signal transmitted over a wireless channel. We show that Magmaw causes significant performance degradation even in the presence of strong defense mechanisms.
• Score: 21.878711118514342
• License:
• Abstract: Machine Learning (ML) has been instrumental in enabling joint transceiver optimization by merging all physical layer blocks of the end-to-end wireless communication systems. Although there have been a number of adversarial attacks on ML-based wireless systems, the existing methods do not provide a comprehensive view including multi-modality of the source data, common physical layer protocols, and wireless domain constraints. This paper proposes Magmaw, a novel wireless attack methodology capable of generating universal adversarial perturbations for any multimodal signal transmitted over a wireless channel. We further introduce new objectives for adversarial attacks on downstream applications. We adopt the widely-used defenses to verify the resilience of Magmaw. For proof-of-concept evaluation, we build a real-time wireless attack platform using a software-defined radio system. Experimental results demonstrate that Magmaw causes significant performance degradation even in the presence of strong defense mechanisms. Furthermore, we validate the performance of Magmaw in two case studies: encrypted communication channel and channel modality-based ML model.

Related papers

• WDMoE: Wireless Distributed Large Language Models with Mixture of Experts [65.57581050707738]
  We propose a wireless distributed Large Language Models (LLMs) paradigm based on Mixture of Experts (MoE) We decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at base station (BS) and mobile devices. We design an expert selection policy by taking into account both the performance of the model and the end-to-end latency.
  arXiv  Detail & Related papers  (2024-05-06T02:55:50Z)
• Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training [62.77129284830945]
  This paper considers a regression problem in a wireless setting and shows that adversarial attacks can break the DL-based approach. We also analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly.
  arXiv  Detail & Related papers  (2022-06-14T04:55:11Z)
• An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks [0.0]
  We develop an intelligent, efficient, and updatable intrusion detection system by applying an important machine learning concept known as ensemble learning. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84% and 97.2%, respectively.
  arXiv  Detail & Related papers  (2022-04-28T23:10:47Z)
• Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression [1.218340575383456]
  Future communication networks must address the scarce spectrum to accommodate growth of heterogeneous wireless devices. We exploit the potential of deep neural networks based multi-task learning framework to simultaneously learn modulation and signal classification tasks. We provide a comprehensive heterogeneous wireless signals dataset for public use.
  arXiv  Detail & Related papers  (2022-02-26T14:51:02Z)
• Distributed Learning in Wireless Networks: Recent Progress and Future Challenges [170.35951727508225]
  Next-generation wireless networks will enable many machine learning (ML) tools and applications to analyze various types of data collected by edge devices. Distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges. This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks.
  arXiv  Detail & Related papers  (2021-04-05T20:57:56Z)
• Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network [62.77129284830945]
  We show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. We benchmark the performance of these attacks and show that with a small perturbation in the input of the neural network (NN), the white-box attacks can result in infeasible solutions up to 86%.
  arXiv  Detail & Related papers  (2021-01-28T16:18:19Z)
• Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications [1.647426214278143]
  Distributed machine learning (DML) techniques have been increasingly applied to wireless communications. The unique features of wireless systems, such as large scale, geographically dispersed deployment, user mobility, and massive amount of data, give rise to new challenges in the design of DML techniques. This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks.
  arXiv  Detail & Related papers  (2020-12-02T19:53:32Z)
• Wireless for Machine Learning [91.13476340719087]
  We give an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support machine learning services over distributed datasets. There are two clear themes within the literature, analog over-the-air computation and digital radio resource management optimized for ML. This survey gives a comprehensive introduction to these methods, reviews the most important works, highlights open problems, and discusses application scenarios.
  arXiv  Detail & Related papers  (2020-08-31T11:09:49Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)
• When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions [14.040811989589741]
  Wireless systems are vulnerable to various attacks such as jamming and eavesdropping due to the shared and broadcast nature of wireless medium. To support both attack and defense strategies, machine learning (ML) provides automated means to learn from and adapt to wireless communication characteristics. This article discusses motivation, background, and scope of research efforts that bridge ML and wireless security.
  arXiv  Detail & Related papers  (2020-01-24T05:07:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.