Browsing by Author Thien van, Luong
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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 ... |
