Adversarial Machine Learning Attack on Modulation Classification

Usama, Muhammad; Asim, Muhammad; Qadir, Junaid; Al-Fuqaha, Ala; Imran, Muhammad Ali

doi:10.1109/ucet.2019.8881843

Cited by 17 publications

(11 citation statements)

References 19 publications

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“…While FGSM is a computationally cheap method for creating adversarial examples, the large body of literature in adversarial ML for CV has yielded algorithms that can evade classifiers with even smaller perturbations. In [88], a more sophisticated adversarial methodology was used to carry out an attack on AMC [99]. Not only was this attack successful for a DNN, but, when the adversarial examples were input to classifiers not based on DNNs (i.e.…”

Section: ) Untargeted Digital Attacksmentioning

confidence: 99%

“…Therefore, both the transmission and perturbation are impacted by channel effects, hardware impairments at both the transmitter and receiver, and DSP pre-preprocessing techniques used before reaching the DNN for classification (a physical attack in Figure 4). All of these can serve as an impediment for an attacker, forcing them to raise their adversarial perturbation power [85], [86], [88], [114]. Additionally, so-called white-box attacks, which assume full knowledge of the target DNN, are generally known to be more effective than black-box attacks which assume close to zero knowledge about the target, regardless of modality.…”

Section: ) Becoming Robust To Attacksmentioning

confidence: 99%

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An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

Wong

Clark

Flowers

et al. 2021

IEEE Open J. Commun. Soc.

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While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications, a field loosely termed Radio Frequency Machine Learning (RFML). In particular, recent research has shown DL to be an enabling technology for Cognitive Radio (CR) applications as well as a useful tool for supplementing expertly defined algorithms for spectrum awareness applications such as signal detection, estimation, and classification. A major driver for the usage of RFML is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative raw Radio Frequency (RF) data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware requirements, that must be taken into account before deploying RFML systems in real-world wireless communication applications that largely go unaddressed in the current literature. This paper examines the prior works related to these major research considerations, with focus on the dependencies between them and factors unique to the RFML space.

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Section: ) Untargeted Digital Attacksmentioning

confidence: 99%

Section: ) Becoming Robust To Attacksmentioning

confidence: 99%

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

Wong

Clark

Flowers

et al. 2021

IEEE Open J. Commun. Soc.

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“…Similar to other DNN-based applications, DNN-based wireless applications are susceptible to adversarial attacks [1]- [4], [6], [7], [12], [14], [15], [31]- [34], [36]. Flowers et al [7] use the FGSM method to evaluate the vulnerabilities of the raw in-phase and quadrant (IQ) based automatic modulation classification task.…”

Section: B Adversarial Examplesmentioning

confidence: 99%

Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems

Bahramali

Nasr

Houmansadr

et al. 2021

Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

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Deep Neural Networks (DNNs) have become prevalent in wireless communication systems due to their promising performance. However, similar to other DNN-based applications, they are vulnerable to adversarial examples. In this work, we propose an input-agnostic, undetectable, and robust adversarial attack against DNN-based wireless communication systems in both white-box and black-box scenarios. We design tailored Universal Adversarial Perturbations (UAPs) to perform the attack. We also use a Generative Adversarial Network (GAN) to enforce an undetectability constraint for our attack. Furthermore, we investigate the robustness of our attack against countermeasures. We show that in the presence of defense mechanisms deployed by the communicating parties, our attack performs significantly better compared to existing attacks against DNN-based wireless systems. In particular, the results demonstrate that even when employing well-considered defenses, DNN-based wireless communications are vulnerable to adversarial attacks.

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“…Discussions of Adversarial ML [161], [162] date back at least 15 years [163]- [165] and have broadened to include exploratory attacks that seek to learn information about (or replicate) the classifier [166] or training data [167] through limited probes on the model to observe it's input/output relationship. However, the most recent explosion in concern for the vulnerabilities of DNNs specifically is largely credited to the Fast Gradient Sign Method (FGSM) [133] [153], [154] [155]- [159] Figure 3: Threat Model for RFML adopted from [156], [160] and including related work.…”

Section: A Adversarial Machine Learningmentioning

confidence: 99%

“…While FGSM is a computationally cheap method for creating adversarial examples, the large body of literature in adversarial ML for CV has yielded algorithms that can evade classifiers with even smaller perturbations. In [159], a more sophisticated adversarial methodology [169] was used to carry out an attack on AMC; not only was this attack successful for a DNN, but, when the adversarial examples were input to classifiers not based on DNNs (e.g. Support Vector Machine (SVM), Decision Trees, Random Forests, etc.)…”

Section: A Adversarial Machine Learningmentioning

confidence: 99%

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

Wong,

Clark,

Flowers

et al. 2020

Preprint

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While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications. In particular, recent research has shown deep machine learning to be an enabling technology for cognitive radio applications as well as a useful tool for supplementing expertly defined algorithms for spectrum sensing applications such as signal detection, estimation, and classification (termed here as Radio Frequency Machine Learning, or RFML). A major driver for the usage of deep machine learning in the context of wireless communications is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware/software issues, that must be taken into account before deploying deep machine learning systems in real-world wireless communication applications. This paper provides an overview and survey of prior work related to these major research considerations. In particular, we present their unique considerations in the RFML application space, which are not generally present in the image, audio, and/or text application spaces.

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Adversarial Machine Learning Attack on Modulation Classification

Cited by 17 publications

References 19 publications

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

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