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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IEEE Transactions on Magnetics
2022
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| Truy cập trực tuyến: | 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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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 |
8.891787 |