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Detection of Spoofing Attacks in Aeronautical Ad-Hoc Networks Using Deep Autoencoders

We consider an aeronautical ad-hoc network relying on aeroplanes operating in the presence of a spoofer. The aggregated signal received by the terrestrial base station is considered as “clean” or “normal”, if the legitimate aeroplanes transmit their signals and there is no spoofing attack. By contra...

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Main Authors: Tiep M. Hoang, Trinh, van Chien, Thien van, Luong
Format: Bài trích
Language:English
Published: IEEE Transactions on Magnetics 2022
Subjects:
PHY authentication
Spoofing detection
Online Access:https://ieeexplore.ieee.org/document/9724185/keywords#keywords
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5889
https://doi.org/10.1109/tifs.2022.3155970
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spelling oai:localhost:PNK-58892022-08-17T05:54:55Z Detection of Spoofing Attacks in Aeronautical Ad-Hoc Networks Using Deep Autoencoders Tiep M. Hoang Trinh, van Chien Thien van, Luong PHY authentication Spoofing detection We consider an aeronautical ad-hoc network relying on aeroplanes operating in the presence of a spoofer. The aggregated signal received by the terrestrial base station is considered as “clean” or “normal”, if the legitimate aeroplanes transmit their signals and there is no spoofing attack. By contrast, the received signal is considered as “spurious” or “abnormal” in the face of a spoofing signal. An autoencoder (AE) is trained to learn the characteristics/features from a training dataset, which contains only normal samples associated with no spoofing attacks. The AE takes original samples as its input samples and reconstructs them at its output. Based on the trained AE, we define the detection thresholds of our spoofing discovery algorithm. To be more specific, contrasting the output of the AE against its input will provide us with a measure of geometric waveform similarity/dissimilarity in terms of the peaks of curves 2022-07-13T01:59:49Z 2022-07-13T01:59:49Z 2022 Bài trích https://ieeexplore.ieee.org/document/9724185/keywords#keywords https://dlib.phenikaa-uni.edu.vn/handle/PNK/5889 https://doi.org/10.1109/tifs.2022.3155970 en IEEE Transactions on Magnetics
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic PHY authentication
Spoofing detection
spellingShingle PHY authentication
Spoofing detection
Tiep M. Hoang
Trinh, van Chien
Thien van, Luong
Detection of Spoofing Attacks in Aeronautical Ad-Hoc Networks Using Deep Autoencoders
description We consider an aeronautical ad-hoc network relying on aeroplanes operating in the presence of a spoofer. The aggregated signal received by the terrestrial base station is considered as “clean” or “normal”, if the legitimate aeroplanes transmit their signals and there is no spoofing attack. By contrast, the received signal is considered as “spurious” or “abnormal” in the face of a spoofing signal. An autoencoder (AE) is trained to learn the characteristics/features from a training dataset, which contains only normal samples associated with no spoofing attacks. The AE takes original samples as its input samples and reconstructs them at its output. Based on the trained AE, we define the detection thresholds of our spoofing discovery algorithm. To be more specific, contrasting the output of the AE against its input will provide us with a measure of geometric waveform similarity/dissimilarity in terms of the peaks of curves
format Bài trích
author Tiep M. Hoang
Trinh, van Chien
Thien van, Luong
author_facet Tiep M. Hoang
Trinh, van Chien
Thien van, Luong
author_sort Tiep M. Hoang
title Detection of Spoofing Attacks in Aeronautical Ad-Hoc Networks Using Deep Autoencoders
title_short Detection of Spoofing Attacks in Aeronautical Ad-Hoc Networks Using Deep Autoencoders
title_full Detection of Spoofing Attacks in Aeronautical Ad-Hoc Networks Using Deep Autoencoders
title_fullStr Detection of Spoofing Attacks in Aeronautical Ad-Hoc Networks Using Deep Autoencoders
title_full_unstemmed Detection of Spoofing Attacks in Aeronautical Ad-Hoc Networks Using Deep Autoencoders
title_sort detection of spoofing attacks in aeronautical ad-hoc networks using deep autoencoders
publisher IEEE Transactions on Magnetics
publishDate 2022
url https://ieeexplore.ieee.org/document/9724185/keywords#keywords
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5889
https://doi.org/10.1109/tifs.2022.3155970
_version_ 1751856286298800128
score 8.887836

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