Adversarial Machine Learning in Wireless Communications using RF Data: A Review

Adesina, Damilola; Hsieh, Chung-Chu; Sagduyu, Yalin E.; Qian, Lijun

doi:10.48550/arxiv.2012.14392

Cited by 9 publications

(8 citation statements)

References 145 publications

(139 reference statements)

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“…Although the susceptibility of deep learning AMC classifiers to evasion attacks has been demonstrated in prior work [12]- [15], relatively few defenses have been proposed to mitigate the effects of adversarial interference [29]. The defense algorithms which have been proposed for AMC DL classifiersadversarial training [30], [31], Gaussian smoothing [32], [33], and autoencoder pre-training [34] -have each demonstrated degraded performance in black box environments.…”

Section: Related Workmentioning

confidence: 99%

A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification

Sahay¹,

Brinton²,

Love³

2021

Preprint

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Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted (non-random) wireless interference into transmitted signals to induce erroneous classification predictions. Furthermore, adversarial interference is transferable in black box environments, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification model. In this work, we propose a novel wireless receiver architecture to mitigate the effects of adversarial interference in various black box attack environments. We begin by evaluating the architecture uncertainty environment, where we show that adversarial attacks crafted to fool specific AMC DL architectures are not directly transferable to different DL architectures. Next, we consider the domain uncertainty environment, where we show that adversarial attacks crafted on time domain and frequency domain features to not directly transfer to the altering domain. Using these insights, we develop our Assorted Deep Ensemble (ADE) defense, which is an ensemble of deep learning architectures trained on time and frequency domain representations of received signals. Through evaluation on two wireless signal datasets under different sources of uncertainty, we demonstrate that our ADE obtains substantial improvements in AMC classification performance compared with baseline defenses across different adversarial attacks and potencies.

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Section: Related Workmentioning

confidence: 99%

A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification

Sahay¹,

Brinton²,

Love³

2021

Preprint

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“…In general, ML is known to be susceptible to manipulations of the inputs in training and test times, as commonly studied under adversarial machine learning (AML) [15]. AML attacks have been applied to the wireless domain [16]- [18]. These attacks include inference (exploratory) attacks [19]- [21], evasion (adversarial) attacks [22]- [42], poisoning (causative) attacks [43]- [47], Trojan attacks [48], spoofing attacks [49]- [52], membership inference attacks [53], [54], and attacks to facilitate covert communications [55]- [57].…”

Section: Introductionmentioning

confidence: 99%

Jamming Attacks on Federated Learning in Wireless Networks

Shi¹,

Sagduyu²

2022

Preprint

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Federated learning (FL) offers a decentralized learning environment so that a group of clients can collaborate to train a global model at the server, while keeping their training data confidential. This paper studies how to launch over-the-air jamming attacks to disrupt the FL process when it is executed over a wireless network. As a wireless example, FL is applied to learn how to classify wireless signals collected by clients (spectrum sensors) at different locations (such as in cooperative sensing). An adversary can jam the transmissions for the local model updates from clients to the server (uplink attack), or the transmissions for the global model updates the server to clients (downlink attack), or both. Given a budget imposed on the number of clients that can be attacked per FL round, clients for the (uplink/downlink) attack are selected according to their local model accuracies that would be expected without an attack or ranked via spectrum observations. This novel attack is extended to general settings by accounting different processing speeds and attack success probabilities for clients. Compared to benchmark attack schemes, this attack approach degrades the FL performance significantly, thereby revealing new vulnerabilities of FL to jamming attacks in wireless networks.

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“…Due to the shared and open nature of wireless medium, wireless applications are highly susceptible to adversaries such as jammers and eavesdroppers that can manipulate the training and testing processes of machine learning over the air. While there is a growing interest in designing attacks on machine learningdriven data and control planes of wireless communications [6]- [9], adversarial machine learning has not been considered yet for sophisticated communication systems such as 5G.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Machine Learning for 5G Communications Security

Sagduyu

Erpek

Shi

2021

Preprint

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Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization. As communication systems become smarter with cognitive radio capabilities empowered by machine learning to perform critical tasks such as spectrum awareness and spectrum sharing, they also become susceptible to new vulnerabilities due to the attacks that target the machine learning applications. This paper identifies the emerging attack surface of adversarial machine learning and corresponding attacks launched against wireless communications in the context of 5G systems. The focus is on attacks against (i) spectrum sharing of 5G communications with incumbent users such as in the Citizens Broadband Radio Service (CBRS) band and (ii) physical layer authentication of 5G User Equipment (UE) to support network slicing. For the first attack, the adversary transmits during data transmission or spectrum sensing periods to manipulate the signal-level inputs to the deep learning classifier that is deployed at the Environmental Sensing Capability (ESC) to support the 5G system. For the second attack, the adversary spoofs wireless signals with the generative adversarial network (GAN) to infiltrate the physical layer authentication mechanism based on a deep learning classifier that is deployed at the 5G base station. Results indicate major vulnerabilities of 5G systems to adversarial machine learning. To sustain the 5G system operations in the presence of adversaries, a defense mechanism is presented to increase the uncertainty of the adversary in training the surrogate model used for launching its subsequent attacks.

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Adversarial Machine Learning in Wireless Communications using RF Data: A Review

Cited by 9 publications

References 145 publications

A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification

A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification

Jamming Attacks on Federated Learning in Wireless Networks

Adversarial Machine Learning for 5G Communications Security

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