Secure Design of Cyber-Physical Systems at the Radio Frequency Level: Machine and Deep Learning-Driven Approaches, Challenges and Opportunities

Comert, Ceren; Gül, Ömer Melih; Kulhandjian, Michel; Touazi, Azzedine; Ellement, Cliff; Kantarcı, Burak; D’Amours, Claude

doi:10.1007/978-3-031-16237-4_6

Cited by 5 publications

(3 citation statements)

References 89 publications

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“…Channel impairments and environmental circumstances affect accuracies of DL-based techniques while detecting RF fingerprints of transmitters [18], [19]. As it is infeasible to collect data in all environmental conditions, augmentationbased approaches for RF fingerprinting can be considered as an efficient tool to deal with channel impairments.…”

Section: A Motivationmentioning

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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Section: A Motivationmentioning

confidence: 99%

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

et al. 2023

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“…Channel impairments affect the performance of DL techniques to identify the RF signatures of a transmitter [8,9]. It is not feasible to collect data in all circumstances.…”

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 multi-robot systems which employ RF-domain security solutions may need computational and so energy resources if they use deep learning methods, as explained in [ 17 , 18 , 19 , 20 ]. Hence, multi-robot systems need more energy-efficient solutions for task execution.…”

Section: Introductionmentioning

confidence: 99%

Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks

Gül

2023

Sensors

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In this work, we investigate an energy-aware multi-robot task-allocation (MRTA) problem in a cluster of the robot network that consists of a base station and several clusters of energy-harvesting (EH) robots. It is assumed that there are M+1 robots in the cluster and M tasks exist in each round. In the cluster, a robot is elected as the cluster head, which assigns one task to each robot in that round. Its responsibility (or task) is to collect the resultant data from the remaining M robots to aggregate and transmit directly to the BS. This paper aims to allocate the M tasks to the remaining M robots optimally or near optimally by considering the distance to be traveled by each node, the energy required for executing each task, the battery level at each node, and the energy-harvesting capabilities of the nodes. Then, this work presents three algorithms: Classical MRTA Approach, Task-aware MRTA Approach, EH and Task-aware MRTA Approach. The performances of the proposed MRTA algorithms are evaluated under both independent and identically distributed (i.i.d.) and Markovian energy-harvesting processes for different scenarios with five robots and 10 robots (with the same number of tasks). EH and Task-aware MRTA Approach shows the best performance among all MRTA approaches by keeping up to 100% more energy in the battery than the Classical MRTA Approach and keeping up to 20% more energy in the battery than the Task-aware MRTA Approach.

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Secure Design of Cyber-Physical Systems at the Radio Frequency Level: Machine and Deep Learning-Driven Approaches, Challenges and Opportunities

Cited by 5 publications

References 89 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

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

Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks

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