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dc.contributor.authorShafique, Arslan
dc.contributor.authorMehmood, Abid
dc.contributor.authorElhadef, Mourad
dc.identifier.citationA. Shafique, A. Mehmood and M. Elhadef, "Detecting Signal Spoofing Attack in UAVs Using Machine Learning Models," in IEEE Access, vol. 9, pp. 93803-93815, 2021,en_US
dc.description.abstractDue to the tremendous advancement in interactive multimedia systems and technologies, security has become a major aspect. Advanced technology can be utilized for hacking autonomous systems like Unmanned Aerial vehicles (UAVs) in different ways such as spoofing and jamming. It can be spoofed by the injection of fake signals into the sensors. For the protection of the UAVs from the Global Positioning System (GPS) signal spoofing attack, we propose a new methodology by incorporating a machine learning (ML) algorithm such as Support Vector Machine (SVM). A detailed analysis of several learning algorithms is also carried out to choose the suitable learning algorithm for the proposed work. Once the suitable ML algorithm is selected, we perform K-fold analyses to develop other learning models by choosing different values of K-folds thus we called them K-learning models. The purpose of developing K-learning models is to apply voting techniques to the developed K-learning models. Moreover, the signal features used in the proposed work are jitter, jitter (absolute), jitter (local), jitter (RAP), jitter (ppq5), shimmer, shimmer (local), shimmer (dB), shimmer (apq3), shimmer (apq5) and frequency modulation. Based on these features of the signal, we train our proposed model for the detection of counterfeit GPS signals. To gauge the performance of the proposed model, we perform different experimentation analyses such as accuracy, precision, recall, and F1-score. The results and analysis show the effectiveness of the proposed work over existing techniques.en_US
dc.subjectData privacyen_US
dc.subjectPerturbation methodsen_US
dc.titleDetecting signal spoofing attack in uavs using machine learning modelsen_US
dc.title.alternativeJournal Articleen_US

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