# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 6 Conclusions  
In this study, we tackled one of the most prevalent failures related to UAVs' aerial navigation system, known as a GPS spoofing attack. Indeed, malicious GPS signals may disrupt UAV navigation systems, causing damage. In our study, we worked on a well-known dataset in this area. We extracted data sequences from three missions with different sizes. We handled thirteen features collected from eight channels of a GPS receiver. Then, we evaluated the efficiency of tree-based classifiers, namely decision tree, random forest, and XGBoost, over numerous independent tests in classifying authentic GPS signals against simplistic, intermediate, and sophisticated spoofed signals. Additionally, we leveraged the feature importance attribute of our tree-based models to extract the relevant features in detecting such attacks. Very interesting results were demonstrated by the random forest  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Reference** & **Model** & \
\begin{tabular}{c} **Multi Attack** \\ **Classification** \\ \end{tabular} & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline SatUAV & [30] & CNN & X & 94.80\% & 93.00\% & 97.90\% & 95.40\% \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Real-time \\ GPS signals \\ \end{tabular} } & [36] & SVM–CNN & X & 99.72\% & 99.65\% & 99.77\% & 99.72\% \\ \cline{2-8}  & Our best model & RF & \(\surd\) & 99.99\% & 99.98\% & 99.98\% & 99.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparative analysis between our proposed method and state-of-the-art approaches.  
model, achieving an average accuracy of 99.99%, outperforming the existing methods in the literature. In addition to that, we were able to distinguish precisely between the attack types compared. This finding is very different from those in the literature. Indeed, none of the studied works were able to detect the attack type. However, despite the contribution provided in this work, many challenges require further investigation. In our future work, we intend to assess the generalization capability of our proposed models tuned on simulated GPS spoofing data, with real-world GPS spoofing attacks. We also aim to investigate mitigation spoofing mechanisms in order to help UAVs manage the security of their navigation.  
Methodology, A.K. and H.F.; investigation, M.M.; writing--original draft preparation, M.M.; writing--review and editing, M.A.R., A.K. and H.F.; supervision, H.F. All authors have read and agreed to the published version of the manuscript.  
This research has not received any external funding.  
Not applicable.  
The dataset used in this paper is publicly available in the link: [[https://ieee-dataport.org/documents/dataset-gps-spoofing-detection-autonomous-vehicles](https://ieee-dataport.org/documents/dataset-gps-spoofing-detection-autonomous-vehicles)]([https://ieee-dataport.org/documents/dataset-gps-spoofing-detection-autonomous-vehicles](https://ieee-dataport.org/documents/dataset-gps-spoofing-detection-autonomous-vehicles)).  
The authors declare no conflicts of interest.