Device Authentication Codes based on RF Fingerprinting using Deep Learning

Bassey, Joshua; Li, Xiangfang; Qian, Lijun

doi:10.4108/eai.30-11-2021.172305

Cited by 8 publications

(3 citation statements)

References 44 publications

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“…[44] It has also been shown that complex-valued neural networks have potential in domains where the data is naturally represented with complex numbers, or the problem is complex by design. [45] Thus, using such complex networks could also potentially be better in dealing with quantum-mechanical problems, as quantum mechanics is inherently a complex-valued theory. However, it is typically not efficient to implement a complex-valued network on a classical digital computer as complex numbers have to be represented by two real numbers on the digital computer, [46] which increases the computationally expensive components of the neural network algorithms.…”

Section: Discussionmentioning

confidence: 99%

Molecular Property Prediction with Photonic Chip‐Based Machine Learning

Zhang

Lau

Wan

et al. 2022

Laser & Photonics Reviews

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Machine learning methods have revolutionized the discovery process of new molecules and materials. However, the intensive training process of neural networks for molecules with ever‐increasing complexity has resulted in exponential growth in computation cost, leading to long simulation time and high energy consumption. Photonic chip technology offers an alternative platform for implementing neural networks with faster data processing and lower energy usage compared to digital computers. Photonics technology is naturally capable of implementing complex‐valued neural networks at no additional hardware cost. Here, the capability of photonic neural networks for predicting the quantum mechanical properties of molecules is demonstrated. To the best of knowledge, this work is the first to harness photonic technology for machine learning applications in computational chemistry and molecular sciences, such as drug discovery and materials design. It is further shown that multiple properties can be learned simultaneously in a photonic chip via a multi‐task regression learning algorithm, which is also the first of its kind as well, as most previous works focus on implementing a network in the classification task.

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Section: Discussionmentioning

confidence: 99%

Molecular Property Prediction with Photonic Chip‐Based Machine Learning

Zhang

Lau

Wan

et al. 2022

Laser & Photonics Reviews

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“…Recently, Alladi et al [2] introduce an authentication system based on a physical unclonable function. Bassey et al [3] discuss the use of authentication codes for an Internet of Things (IoT) device, and by leveraging the Kolmogorov-Smirnov test, it determines the legitimacy of the user of an IoT device.…”

Section: Introductionmentioning

confidence: 99%

On the Impact of CDL and TDL Augmentation for RF Fingerprinting Under Impaired Channels

Gul,

Kulhandjian,

Kantarci

et al. 2024

WWRT

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Cyber-physical systems have recently been used in several areas (such as connected and autonomous vehicles) due to their high maneuverability. On the other hand, they are susceptible to cyber-attacks. Radio frequency (RF) fingerprinting emerges as a promising approach. This work aims to analyze the impact of decoupling tapped delay line and clustered delay line (TDL+CDL) augmentation-driven deep learning (DL) on transmitter-specific fingerprints to discriminate malicious users from legitimate ones. This work also considers 5G-only-CDL, WiFi-only-TDL augmentation approaches. RF fingerprinting models are sensitive to changing channels and environmental conditions. For this reason, they should be considered during the deployment of a DL model. Data acquisition can be another option. Nonetheless, gathering samples under various conditions for a train set formation may be quite hard. Consequently, data acquisition may not be feasible. This work uses a dataset that includes 5G, 4G, and WiFi samples, and it empowers a CDL+TDL based augmentation technique in order to boost the learning performance of the DL model. Numerical results show that CDL+TDL, 5G-only-CDL, and WiFi-only-TDL augmentation approaches achieve 87.59%, 81.63%, 79.21% accuracy on unobserved data while TDL/CDL augmentation technique and no augmentation approach result in 77.81% and 74.84% accuracy on unobserved data, respectively.

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“…The work in [10] has proposed a physical unclonable function (PUF)-based authentication framework. The work in [11] proposes authentication code for IoT device authentication. The Kolmogorov-Smirnov test is used to decide the legitimacy of device users.…”

Section: Introductionmentioning

confidence: 99%

Secure Industrial IoT Systems via RF Fingerprinting Under Impaired Channels With Interference and Noise

et al. 2023

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Industrial IoT-enabled critical infrastructures are susceptible to cyber attacks due to their mission-critical deployment. To ensure security by design, radio frequency (RF)-based security is considered an effective way for wirelessly monitored or actuated critical infrastructures. For this purpose, this paper presents a novel augmentation-driven deep learning approach to analyze unique transmitter fingerprints and determine the legitimacy of a user device or transmitter. An RF fingerprinting model is susceptible to various channel and environmental conditions that impact the learning performance of a machine/deep learning model. As data gathering cannot always be considered a feasible alternative, efficient solutions that can tackle the impact of varying propagation channels on learning performance are emergent. This work aims to shed light on the RF fingerprinting problem from a different angle when 4G, 5G, and WiFi data samples are collected from different transmitters by proposing a fine-grained augmentation approach to improve the learning performance of a deep learning model. This work also proposes an enhanced classifier structure following the fine-grained augmentation approach. Results of experiments, conducted on the POWDER dataset, demonstrate promising RF fingerprinting performance when training data are augmented in a waveform-specific fine-grained manner. Thus, the RF identification accuracy can be boosted to 97.84% on unseen RF data instances from our previously published work where we had achieved an accuracy of 87.94% using tapped delay line (TDL)/clustered delay line (CDL)-based augmentation approach. The paper also presents a sensitivity analysis of the fine-grained approach concerning different signal-to-noise-ratio (SNR), signal-to-interference-ratio (SIR) levels (20 dB and 30 dB), and signal-to-interference-plus-noiseratio (SINR) levels (15 dB, 25 dB). The sensitivity analysis exhibits that it achieves 85.78% accuracy at 20 dB SIR on both Day 1 (train) and Day 2 (test) data. In addition, it achieves 92.37% accuracy even at 20 dB SNR on Day 2 data from POWDER dataset. Furthermore, it achieves 84.95% accuracy at 15 dB SINR on Day 2 data. Hence, these results exhibit the resiliency of the fine-grained augmentation approach against interference and noise.INDEX TERMS deep learning, data augmentation, radio frequency fingerprinting, secure design, unmanned aerial vehicles, Internet of Things (IoT)

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Device Authentication Codes based on RF Fingerprinting using Deep Learning

Cited by 8 publications

References 44 publications

Molecular Property Prediction with Photonic Chip‐Based Machine Learning

Molecular Property Prediction with Photonic Chip‐Based Machine Learning

On the Impact of CDL and TDL Augmentation for RF Fingerprinting Under Impaired Channels

Secure Industrial IoT Systems via RF Fingerprinting Under Impaired Channels With Interference and Noise

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