RF-NeuralNet: Lightweight Deep Learning Framework for Detecting Rogue Drones from Radio Frequency Signatures

Maham Misbah; Mahnoor Dil; Waqas Khalid; Zeeshan Kaleem

doi:10.1109/ICACR59381.2023.10314637

RF-NeuralNet: Lightweight Deep Learning Framework for Detecting Rogue Drones from Radio Frequency Signatures

Maham Misbah^*, Mahnoor Dil^*, Waqas Khalid, Zeeshan Kaleem^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Unmanned aerial vehicles (UAVs) have emerged as a revolutionary technology with diverse applications in fields such as crop monitoring, logistics, and traffic surveillance. Despite all these advantages, they also pose certain challenges such as privacy breaches, potential collision risks with airplanes, and terrorism activities. To mitigate these concerns, various techniques have been developed for UAV detection. In this paper, we propose a computationally efficient deep learning network RF-NeuralNet for UAV detection and mode identification using RF fingerprints. The proposed network involves a multiple-level skip connection to mitigate the gradient vanishing problem and multiple-level pooling layers for deep-level feature extraction. We evaluate the performance of the proposed RF-NeuralNet based on multiple UAV monitoring tasks (i.e., UAV identification, classification, and operational mode). Our proposed framework outperformed other state-of-the-art models and achieved an overall accuracy of 89%.

Original language	English
Title of host publication	2023 7th International Conference on Automation, Control and Robots, ICACR 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	163-167
Number of pages	5
ISBN (Electronic)	9798350302882
DOIs	https://doi.org/10.1109/ICACR59381.2023.10314637
State	Published - 2023
Externally published	Yes
Event	7th International Conference on Automation, Control and Robots, ICACR 2023 - Hybrid, Kuala Lumpur, Malaysia Duration: 4 Aug 2023 → 6 Aug 2023

Publication series

Name	2023 7th International Conference on Automation, Control and Robots, ICACR 2023

Conference

Conference	7th International Conference on Automation, Control and Robots, ICACR 2023
Country/Territory	Malaysia
City	Hybrid, Kuala Lumpur
Period	4/08/23 → 6/08/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Drones
Multiclass classification
Neural Net
Radio frequency

ASJC Scopus subject areas

Mechanical Engineering
Control and Optimization
Computational Mathematics

Access to Document

10.1109/ICACR59381.2023.10314637

Cite this

Misbah, M., Dil, M., Khalid, W., & Kaleem, Z. (2023). RF-NeuralNet: Lightweight Deep Learning Framework for Detecting Rogue Drones from Radio Frequency Signatures. In 2023 7th International Conference on Automation, Control and Robots, ICACR 2023 (pp. 163-167). (2023 7th International Conference on Automation, Control and Robots, ICACR 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACR59381.2023.10314637

Misbah, Maham ; Dil, Mahnoor ; Khalid, Waqas et al. / RF-NeuralNet : Lightweight Deep Learning Framework for Detecting Rogue Drones from Radio Frequency Signatures. 2023 7th International Conference on Automation, Control and Robots, ICACR 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 163-167 (2023 7th International Conference on Automation, Control and Robots, ICACR 2023).

@inproceedings{1d2fa8d2287147c9a9fc3bfe7681e722,

title = "RF-NeuralNet: Lightweight Deep Learning Framework for Detecting Rogue Drones from Radio Frequency Signatures",

abstract = "Unmanned aerial vehicles (UAVs) have emerged as a revolutionary technology with diverse applications in fields such as crop monitoring, logistics, and traffic surveillance. Despite all these advantages, they also pose certain challenges such as privacy breaches, potential collision risks with airplanes, and terrorism activities. To mitigate these concerns, various techniques have been developed for UAV detection. In this paper, we propose a computationally efficient deep learning network RF-NeuralNet for UAV detection and mode identification using RF fingerprints. The proposed network involves a multiple-level skip connection to mitigate the gradient vanishing problem and multiple-level pooling layers for deep-level feature extraction. We evaluate the performance of the proposed RF-NeuralNet based on multiple UAV monitoring tasks (i.e., UAV identification, classification, and operational mode). Our proposed framework outperformed other state-of-the-art models and achieved an overall accuracy of 89\%.",

keywords = "Drones, Multiclass classification, Neural Net, Radio frequency",

author = "Maham Misbah and Mahnoor Dil and Waqas Khalid and Zeeshan Kaleem",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 7th International Conference on Automation, Control and Robots, ICACR 2023 ; Conference date: 04-08-2023 Through 06-08-2023",

year = "2023",

doi = "10.1109/ICACR59381.2023.10314637",

language = "English",

series = "2023 7th International Conference on Automation, Control and Robots, ICACR 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "163--167",

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address = "United States",

}

Misbah, M, Dil, M, Khalid, W & Kaleem, Z 2023, RF-NeuralNet: Lightweight Deep Learning Framework for Detecting Rogue Drones from Radio Frequency Signatures. in 2023 7th International Conference on Automation, Control and Robots, ICACR 2023. 2023 7th International Conference on Automation, Control and Robots, ICACR 2023, Institute of Electrical and Electronics Engineers Inc., pp. 163-167, 7th International Conference on Automation, Control and Robots, ICACR 2023, Hybrid, Kuala Lumpur, Malaysia, 4/08/23. https://doi.org/10.1109/ICACR59381.2023.10314637

RF-NeuralNet: Lightweight Deep Learning Framework for Detecting Rogue Drones from Radio Frequency Signatures. / Misbah, Maham; Dil, Mahnoor; Khalid, Waqas et al.
2023 7th International Conference on Automation, Control and Robots, ICACR 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 163-167 (2023 7th International Conference on Automation, Control and Robots, ICACR 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Misbah, Maham

AU - Dil, Mahnoor

AU - Khalid, Waqas

AU - Kaleem, Zeeshan

PY - 2023

Y1 - 2023

N2 - Unmanned aerial vehicles (UAVs) have emerged as a revolutionary technology with diverse applications in fields such as crop monitoring, logistics, and traffic surveillance. Despite all these advantages, they also pose certain challenges such as privacy breaches, potential collision risks with airplanes, and terrorism activities. To mitigate these concerns, various techniques have been developed for UAV detection. In this paper, we propose a computationally efficient deep learning network RF-NeuralNet for UAV detection and mode identification using RF fingerprints. The proposed network involves a multiple-level skip connection to mitigate the gradient vanishing problem and multiple-level pooling layers for deep-level feature extraction. We evaluate the performance of the proposed RF-NeuralNet based on multiple UAV monitoring tasks (i.e., UAV identification, classification, and operational mode). Our proposed framework outperformed other state-of-the-art models and achieved an overall accuracy of 89%.

AB - Unmanned aerial vehicles (UAVs) have emerged as a revolutionary technology with diverse applications in fields such as crop monitoring, logistics, and traffic surveillance. Despite all these advantages, they also pose certain challenges such as privacy breaches, potential collision risks with airplanes, and terrorism activities. To mitigate these concerns, various techniques have been developed for UAV detection. In this paper, we propose a computationally efficient deep learning network RF-NeuralNet for UAV detection and mode identification using RF fingerprints. The proposed network involves a multiple-level skip connection to mitigate the gradient vanishing problem and multiple-level pooling layers for deep-level feature extraction. We evaluate the performance of the proposed RF-NeuralNet based on multiple UAV monitoring tasks (i.e., UAV identification, classification, and operational mode). Our proposed framework outperformed other state-of-the-art models and achieved an overall accuracy of 89%.

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ER -

Misbah M, Dil M, Khalid W, Kaleem Z. RF-NeuralNet: Lightweight Deep Learning Framework for Detecting Rogue Drones from Radio Frequency Signatures. In 2023 7th International Conference on Automation, Control and Robots, ICACR 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 163-167. (2023 7th International Conference on Automation, Control and Robots, ICACR 2023). doi: 10.1109/ICACR59381.2023.10314637

RF-NeuralNet: Lightweight Deep Learning Framework for Detecting Rogue Drones from Radio Frequency Signatures

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this