Deep-Learning-Based Device Fingerprinting for Increased LoRa-IoT Security: Sensitivity to Network Deployment Changes

Hamdaoui, Bechir; Elmaghbub, Abdurrahman

doi:10.1109/mnet.001.2100553

Cited by 20 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The paper in [37] aims to tackle the sensitivity of LoRaenabled device fingerprinting to various network and RF propagation effects, through an experimental framework. Firstly, it describes RF datasets collected via LoRa-enabled wireless device testbed.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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)

show abstract

Section: Related Workmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Hence, any considerable change in the training settings yields a different domain. In [5], [6], we conducted an experimental study on LoRa devices, which disclosed the sensitivity of DL-RFFP to domain changes. In that work, the models (i) fail to maintain their high accuracy when channel conditions change and (ii) completely lose their classification ability when the protocol configuration or receiver changes.…”

Section: Introductionmentioning

confidence: 99%

ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation

Elmaghbub¹,

Hamdaoui²,

Wong³

2023

Preprint

View full text Add to dashboard Cite

As the journey of 5G standardization is coming to an end, academia and industry have already begun to consider the sixth-generation (6G) wireless networks, with an aim to meet the service demands for the next decade. Deep learning-based RF fingerprinting (DL-RFFP) has recently been recognized as a potential solution for enabling key wireless network applications and services, such as spectrum policy enforcement and network access control. The state-of-the-art DL-RFFP frameworks suffer from a significant performance drop when tested with data drawn from a domain that is different from that used for training data. In this paper, we propose ADL-ID, an unsupervised domain adaption framework that is based on adversarial disentanglement representation to address the temporal domain adaptation for the RFFP task. Our framework has been evaluated on real LoRa and WiFi datasets and showed about 24% improvement in accuracy when compared to the baseline CNN network on short-term temporal adaptation. It also improves the classification accuracy by up to 9% on long-term temporal adaptation. Furthermore, we release a 5-day, 2.1TB, large-scale WiFi 802.11b dataset collected from 50 Pycom devices to support the research community efforts in developing and validating robust RFFP methods.

show abstract

“…It has been, recently, utilized for device discovery, vulnerability analysis, anomaly detection, and trust-based policy recommendations [7]. Although DL-based hardware device fingerprinting methods have demonstrated promising results in terms of device identification accuracy, several studies (e.g., [6], [8], [9], [10]) have revealed that many of these methods do not perform well when the testing data is collected under a domain that is different from that used during training. Here, the term domain refers to the network setting/environment (e.g., channel condition, device location, etc.)…”

Section: Introductionmentioning

confidence: 99%

A Needle in a Haystack: Distinguishable Deep Neural Network Features for Domain-Agnostic Device Fingerprinting

Elmaghbub,

Hamdaoui

2023

2023 IEEE Conference on Communications and Network Security (CNS)

View full text Add to dashboard Cite

Next-generation networks aim for comprehensive connectivity, interconnecting humans, machines, devices, and systems seamlessly. This interconnectivity raises concerns about privacy and security, given the potential network-wide impact of a single compromise. To address this challenge, the Zero Trust (ZT) paradigm emerges as a key method for safeguarding network integrity and data confidentiality. This work introduces EPS-CNN, a novel deep-learning-based wireless device identification framework designed to serve as the device authentication layer within the ZT architecture, with a focus on resource-constrained IoT devices. At the core of EPS-CNN, a Convolutional Neural Network (CNN) is utilized to generate the device identity from a unique RF signal representation, known as the Double-Sided Envelope Power Spectrum (EPS), which effectively captures the device-specific hardware characteristics while ignoring deviceunrelated information. Experimental evaluations show that the proposed framework achieves over 99%, 93%, and 95% of testing accuracy when tested in same-domain (day, location, and channel), cross-day, and cross-location scenarios, respectively. Our findings demonstrate the superiority of the proposed framework in enhancing the accuracy, robustness, and adaptability of deep learning-based methods, thus offering a pioneering solution for enabling ZT IoT device identification.

show abstract

Deep-Learning-Based Device Fingerprinting for Increased LoRa-IoT Security: Sensitivity to Network Deployment Changes

Cited by 20 publications

References 14 publications

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

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

ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation

A Needle in a Haystack: Distinguishable Deep Neural Network Features for Domain-Agnostic Device Fingerprinting

Contact Info

Product

Resources

About