Effects of Forward Error Correction on Communications Aware Evasion Attacks

DelVecchio, Matthew; Flowers, Bryse; Headley, William C.

doi:10.1109/pimrc48278.2020.9217343

Cited by 6 publications

(15 citation statements)

References 8 publications

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“…Ongoing work has shown that more sophisticated attacks will emerge to overcome these limitations by incorporating more expert knowledge into the adversarial process such as the channel type [118], channel coding scheme [119], or device type [120]. Another key research direction going forward is identifying the type of information being transmitted from an RFML-enabled device to increase attack effectiveness through targeted attacks on acknowledgement messages or transmission decisions, for example [117], [120].…”

Section: Discussion and Future Workmentioning

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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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: Discussion and Future Workmentioning

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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“…The adversarial scenario considered in this work is illustrated by Figure 1. As previously mentioned, balancing the two conflicting goals of evasion of an eavesdropper and successful communication by an intended receiver is difficult and has been examined in previous work [4,5,10,15]. In this work, the idea of successful perception of the signal at the receiver is driven using metrics such as bit error rate (BER) that indicate the success of the communication.…”

Section: Introductionmentioning

confidence: 99%

“…These three losses are each weighted and summed together to guide the ARNs learning process. The work of [4] expanded on the communications aware architecture first introduced in [5] in order to better utilize forward error correction (FEC), but it was found that the changes to the loss functions and transmitter architecture provided improvements beyond just utilizing FEC. The training framework presented in [4], and illustrated in Figure 2, serves as the foundation for the work presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Each AMN is trained to create adversarial signals for just one modulation scheme at at a time. As previously mentioned, the AMN developed in [4] utilizes three loss functions to train the AMN network, namely:…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1: A wireless communications scenario in which an intended communications link is being eavesdropped by a machine learning based spectrum sensor. The transmitter utilizes an adversarial evasion attack to intelligently "perturb" its signal to evade the eavesdropper [4].…”

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confidence: 99%

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Investigating a spectral deception loss metric for training machine learning-based evasion attacks

DelVecchio

Arndorfer

Headley

2020

Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning

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Adversarial evasion attacks have been very successful in causing poor performance in a wide variety of machine learning applications. One such application is radio frequency spectrum sensing. While evasion attacks have proven particularly successful in this area, they have done so at the detriment of the signal's intended purpose. More specifically, for real-world applications of interest, the resulting perturbed signal that is transmitted to evade an eavesdropper must not deviate far from the original signal, less the intended information is destroyed. Recent work by the authors and others has demonstrated an attack framework that allows for intelligent balancing between these conflicting goals of evasion and communication. However, while these methodologies consider creating adversarial signals that minimize communications degradation, they have been shown to do so at the expense of the spectral shape of the signal. This opens the adversarial signal up to defenses at the eavesdropper such as filtering, which could render the attack ineffective. To remedy this, this work introduces a new spectral deception loss metric that can be implemented during the training process to force the spectral shape to be more in-line with the original signal. As an initial proof of concept, a variety of methods are presented that provide a starting point for this proposed loss. Through performance analysis, it is shown that these techniques are effective in controlling the shape of the adversarial signal. CCS CONCEPTS• Computing methodologies → Neural networks; Supervised learning by classification.

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

Adesina

Hsieh

Sagduyu

et al. 2023

IEEE Commun. Surv. Tutorials

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Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in computational resources and algorithmic designs, deep learning (DL) has found success in performing various wireless communication tasks such as signal recognition, spectrum sensing and waveform design. However, ML in general and DL in particular have been found vulnerable to manipulations thus giving rise to a field of study called adversarial machine learning (AML). Although AML has been extensively studied in other data domains such as computer vision and natural language processing, research for AML in the wireless communications domain is still in its early stage. This paper presents a comprehensive review of the latest research efforts focused on AML in wireless communications while accounting for the unique characteristics of wireless systems. First, the background of AML attacks on deep neural networks is discussed and a taxonomy of AML attack types is provided. Various methods of generating adversarial examples and attack mechanisms are also described. In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented. Finally, as new attacks and defense techniques are developed, recent research trends and the overarching future outlook for AML in nextgeneration wireless communications are discussed.

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Effects of Forward Error Correction on Communications Aware Evasion Attacks

Cited by 6 publications

References 8 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

Investigating a spectral deception loss metric for training machine learning-based evasion attacks

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

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