Secure Industrial IoT Systems via RF Fingerprinting Under Impaired Channels With Interference and Noise
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,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10068504/ |
| _version_ | 1797856510311661568 |
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| author | Omer Melih Gul Michel Kulhandjian Burak Kantarci Azzedine Touazi Cliff Ellement Claude D'amours |
| author_facet | Omer Melih Gul Michel Kulhandjian Burak Kantarci Azzedine Touazi Cliff Ellement Claude D'amours |
| author_sort | Omer Melih Gul |
| collection | DOAJ |
| description | 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-noise-ratio (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. |
| first_indexed | 2024-04-09T20:41:33Z |
| format | Article |
| id | doaj.art-d0dc61eb3313441ea2163a3981537302 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T20:41:33Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d0dc61eb3313441ea2163a39815373022023-03-29T23:00:14ZengIEEEIEEE Access2169-35362023-01-0111262892630710.1109/ACCESS.2023.325726610068504Secure Industrial IoT Systems via RF Fingerprinting Under Impaired Channels With Interference and NoiseOmer Melih Gul0https://orcid.org/0000-0002-0673-7877Michel Kulhandjian1Burak Kantarci2https://orcid.org/0000-0003-0220-7956Azzedine Touazi3https://orcid.org/0000-0003-0786-0014Cliff Ellement4Claude D'amours5https://orcid.org/0000-0001-5769-5868School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, CanadaSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, CanadaSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, CanadaArtificial Intelligence Solutions, ThinkRF, Ottawa, ON, CanadaArtificial Intelligence Solutions, ThinkRF, Ottawa, ON, CanadaSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, CanadaIndustrial 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-noise-ratio (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.https://ieeexplore.ieee.org/document/10068504/Deep learningdata augmentationradio frequency fingerprintingsecure designunmanned aerial vehiclesInternet of Things (IoT) |
| spellingShingle | Omer Melih Gul Michel Kulhandjian Burak Kantarci Azzedine Touazi Cliff Ellement Claude D'amours Secure Industrial IoT Systems via RF Fingerprinting Under Impaired Channels With Interference and Noise IEEE Access Deep learning data augmentation radio frequency fingerprinting secure design unmanned aerial vehicles Internet of Things (IoT) |
| title | Secure Industrial IoT Systems via RF Fingerprinting Under Impaired Channels With Interference and Noise |
| title_full | Secure Industrial IoT Systems via RF Fingerprinting Under Impaired Channels With Interference and Noise |
| title_fullStr | Secure Industrial IoT Systems via RF Fingerprinting Under Impaired Channels With Interference and Noise |
| title_full_unstemmed | Secure Industrial IoT Systems via RF Fingerprinting Under Impaired Channels With Interference and Noise |
| title_short | Secure Industrial IoT Systems via RF Fingerprinting Under Impaired Channels With Interference and Noise |
| title_sort | secure industrial iot systems via rf fingerprinting under impaired channels with interference and noise |
| topic | Deep learning data augmentation radio frequency fingerprinting secure design unmanned aerial vehicles Internet of Things (IoT) |
| url | https://ieeexplore.ieee.org/document/10068504/ |
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