Deep Learning Convolutional Neural Networks for Radio Identification

Riyaz, Shamnaz; Sankhe, Kunal; Ioannidis, Stratis; Chowdhury, Kaushik R.

doi:10.1109/mcom.2018.1800153

Cited by 272 publications

(123 citation statements)

References 7 publications

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“…RFF identification authenticates the wireless devices based on their hardware imperfections resulting from the manufacturing process (see [280]- [282] and references therein). These hardware features are unique, permanent and cannot be tampered with, which are ideal for device authentication.…”

Section: B Rff Identificationmentioning

confidence: 99%

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A New Frontier for IoT Security Emerging From Three Decades of Key Generation Relying on Wireless Channels

et al. 2020

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The Internet of Things (IoT) is a transformative technology, which is revolutionizing our everyday life by connecting everyone and everything together. The massive number of devices are preferably connected wirelessly because of the easy installment and flexible deployment. However, the broadcast nature of the wireless medium makes the information accessible to everyone including malicious users, which should hence be protected by encryption. Unfortunately, the secure and efficient provision of cryptographic keys for low-cost IoT devices is challenging; weak keys have resulted in severe security breaches, as evidenced by numerous notorious cyberattacks. This paper provides a comprehensive survey of lightweight security solutions conceived for IoT, relying on key generation from wireless channels. We first introduce the key generation fundamentals and protocols. We then examine how to apply this emerging technique to secure IoT and demonstrate that key generation relying on the randomness of wireless channels is eminently suitable for IoT. This paper reviews the extensive research efforts in the areas of theoretical modelling, simulation based validation and experimental exploration. We finally discuss the hurdles and challenges that key generation is facing and suggest future work to make key generation a reliable and secure solution to safeguard the IoT.

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Section: B Rff Identificationmentioning

confidence: 99%

“…Sometimes it is challenging to identify and extract the best feature. Hence, deep learning may be adopted to directly process the raw I/Q samples without using a particular feature [282], [288], [289].…”

Section: B Rff Identificationmentioning

confidence: 99%

A New Frontier for IoT Security Emerging From Three Decades of Key Generation Relying on Wireless Channels

et al. 2020

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“…A discussion was carried on the robustness of the proposed model under different channel parameters and scales of training sets. Reference [76] proposed a radio fingerprinting method, which adopted CNN model and IQ dataset for network training. Their method is capable of learning inherent signatures from different wireless transmitters, which are useful for identifying hardware devices.…”

Section: Modulation Recognitionmentioning

confidence: 99%

A Survey on Deep Learning Techniques in Wireless Signal Recognition

Dong

Zhou

et al. 2019

Wireless Communications and Mobile Computing

109

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Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks. Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed. In this paper, a brief overview of signal recognition approaches is presented. More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system. In addition, the opening problems and new challenges in practice are discussed. Finally, a conclusion of existing methods and future trends on signal recognition is given.

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Section: Related Work To Rffmentioning

confidence: 99%

“…Research has also been done in the field of radio identifications, for example in References [11][12][13]. The authors of Reference [11] study the hardware impairments and investigates the Convolutional Neural Networks (CNN). The authors of Reference [12] provide a tutorial of device fingerprinting in a wireless device context, while Reference [13] introduces Permutation Entropy (PE) methods to identify devices by evaluating the level of chaos in the received signals.…”

Section: Related Work To Rffmentioning

confidence: 99%

Identifying GNSS Signals Based on Their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator

Ferre

Wang

Sanz-Abia

et al. 2020

Data

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This is a data descriptor paper for a set of raw GNSS signals collected via roof antennas and Spectracom simulator for general-purpose uses. We give one example of possible data use in the context of Radio Frequency Fingerprinting (RFF) studies for signal-type identification based on front-end hardware characteristics at transmitter or receiver side. Examples are given in this paper of achievable classification accuracy of six of the collected signal classes. The RFF is one of the state-of-the-art, promising methods to identify GNSS transmitters and receivers, and can find future applicability in anti-spoofing and anti-jamming solutions for example. The uses of the provided raw data are not limited to RFF studies, but can extend to uses such as testing GNSS acquisition and tracking, antenna array experiments, and so forth.

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Deep Learning Convolutional Neural Networks for Radio Identification

Cited by 272 publications

References 7 publications

A New Frontier for IoT Security Emerging From Three Decades of Key Generation Relying on Wireless Channels

A New Frontier for IoT Security Emerging From Three Decades of Key Generation Relying on Wireless Channels

A Survey on Deep Learning Techniques in Wireless Signal Recognition

Identifying GNSS Signals Based on Their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator

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