Physical layer authentication of Internet of Things wireless devices using convolutional neural networks and recurrence plots

Baldini, Gianmarco; Giuliani, Raimondo; Dimc, Franc

doi:10.1002/itl2.81

Cited by 17 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To name only a few, Recurrence Plot (RP), Continuous Wavelet Transform (CWT), Short-Time Fourier Transform (STFT), and Hilbert Transform (HT) have been employed to generate images. After transforming, these images were sent to a deep network such as CNN, Deep Neural Network (DNN), Deep Residual Network (DRN), or Multi-Stage Training (MST) for feature extraction and transmitter identification [9], [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Zhou

et al. 2019

2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

View full text Add to dashboard Cite

Radio Frequency Fingerprinting (RFF) is one of the promising passive authentication approaches for improving the security of the Internet of Things (IoT). However, with the proliferation of low-power IoT devices, it becomes imperative to improve the identification accuracy at low SNR scenarios. To address this problem, this paper proposes a general Denoising Au-toEncoder (DAE)-based model for deep learning RFF techniques. Besides, a partially stacking method is designed to appropriately combine the semi-steady and steady-state RFFs of ZigBee devices. The proposed Partially Stacking-based Convolutional DAE (PSC-DAE) aims at reconstructing a high-SNR signal as well as device identification. Experimental results demonstrate that compared to Convolutional Neural Network (CNN), PSCDAE can improve the identification accuracy by 14% to 23.5% at low SNRs (from -10 dB to 5 dB) under Additive White Gaussian Noise (AWGN) corrupted channels. Even at SNR = 10 dB, the identification accuracy is as high as 97.5%.

show abstract

Section: Introductionmentioning

confidence: 99%

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Zhou

et al. 2019

2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

View full text Add to dashboard Cite

show abstract

“…However, CNNs were chosen in some research because of their dependability and robust learning capabilities, which have high accuracy and low loss function during training. Baldini et al [19] used CNN and recurrence plot techniques to develop classification approaches for the physical layer authentication challenge. To identify different devices by utilizing distinctive RF fingerprints, Aminuddin et al [20] presented a methodology based on CNN to secure wireless transmission in a wireless local area network.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Industrial Wireless Communication Security Using Deep Learning Architecture-Based Channel Frequency Response

Alhoraibi,

Alghazzawi,

Alhebshi

et al. 2024

IET Signal Processing

View full text Add to dashboard Cite

Wireless communication plays a crucial role in the automation process in the industrial environment. However, the open nature of wireless communication renders industrial wireless sensor networks susceptible to malicious attacks that impersonate authorized nodes. The heterogeneity of the wireless transmission channel, coupled with hardware and software limitations, further complicates the issue of secure authentication. This form of communication urgently requires a lightweight authentication technique characterized by low complexity and high security, as inadequately secure communication could jeopardize the evolution of industrial devices. These requirements are met through the introduction of physical layer authentication. This article proposes novel deep learning (DL) models designed to enhance physical layer authentication by autonomously learning from the frequency domain without relying on expert features. Experimental results demonstrate the effectiveness of the proposed models, showcasing a significant enhancement in authentication accuracy. Furthermore, the study explores the efficacy of various DL architecture settings and traditional machine learning approaches through a comprehensive comparative analysis.

show abstract

“…A number of scholars have conducted extensive research on the issue of RFF recognition [7][8][9][16][17][18][19][20][21], which is discussed below.…”

Section: Related Workmentioning

confidence: 99%

Research on Illegal Mobile Device Identification Based on Radio Frequency Fingerprint Feature

Shao

Wang³

et al. 2023

Electronics

View full text Add to dashboard Cite

Internet of Things (IoT) technology is widely used in new power systems, and it also provides many new modes for network attacks. Illegal terminal device identification is also a significant topic in the field of wireless authentication technology. Some kinds of power network equipment are located in sparsely populated areas and rely on IoT terminals for real-time monitoring. Attackers use illegal terminals to connect power IoT devices for production monitoring and to carry out network attacks, which may cause serious damage, such as power data theft and misoperation of power network equipment. Radio frequency fingerprint (RFF) can extract hardware features from different devices, and is widely used for device identification and authentication. The area over which power network equipment placed is vast, and there are many wireless communication devices and terminals. It is difficult to identify illegal devices through commonly used network management techniques, thus making it difficult to distinguish between the mobile terminals of employees and illegal terminals in general spectrum screening. In response to the above situation, this paper uses the characteristics of the squared spectrum of random access preamble signals to extract hardware device features, proposes an illegal device identification algorithm based on Gaussian distribution theory, and evaluates its performance. The experimental results show that, when the signal-to-noise ratio (SNR) is greater than 15 dB, the average recognition result is greater than 92%. In addition, the algorithm has low computational complexity and high engineering application value.

show abstract

Physical layer authentication of Internet of Things wireless devices using convolutional neural networks and recurrence plots

Cited by 17 publications

References 10 publications

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Enhancing Industrial Wireless Communication Security Using Deep Learning Architecture-Based Channel Frequency Response

Research on Illegal Mobile Device Identification Based on Radio Frequency Fingerprint Feature

Contact Info

Product

Resources

About