## 5 Experimental Analysis
### Results and Discussion  
First, we demonstrate the numerical analysis of the longitudinal and roll, transverse and pitch, and vertical and yaw motions on jamming and spoofing attacks in Figures 7-9, respectively. These analyses demonstrate jamming and spoofing attacks on the attitude control message of UAVs becoming more effective during UAV travel. Figure 6: Block diagram of overall methodology with inputs and outputs. Second, we analyzed the performance of the applied AI models with respect to predicting cyberattacks for commercial UAM. From Figure 10, it is at most clear that KNN predicted successfully the attacks in UAM. Furthermore, the KNN model also predicted the true positive and true negatives of the attack data and no attack data, as is shown in Figure 10. Figure 11 illustrates that the prediction from the logistic regression model was not entirely correct. Additionally, Figure 11 demonstrates that the model predicted the true positive class correctly but could not predict the true negatives. In that case, the logistic regression model incorrectly predicted the negative class. From Figure 12, it is evident that the random forest classifier accurately predicted the attacks in UAM by the attackers. In addition, the random forest classifier model predicted genuine true positive and true negative values for the attack data and no attack data, as depicted in Figure 12. In Figure 13, the random forest classifier correctly predicted the attackers' strikes against UAM. In addition, as indicated in Figure 13, the decision tree classifier model accurately predicted the real true positive and true negative values for the attack data and no attack data. Figure 14 implies that the LSTM model could not successfully predict the accuracy of the attacks on UAM. Additionally, it reveals that the model accurately identified the true positive class but could not predict the true negatives. Therefore, the logistic regression model incorrectly predicted the negative class. Figure 8: Numerical feature analysis of attacked transverse and pitch motions. Figure 7: Numerical feature analysis of attacked longitudinal and roll motions. Figure 9: Numerical feature analysis of attacked vertical and yaw motions. Figure 11: Confusion matrix on prediction of logistic regression classifier. Figure 12: Confusion matrix on prediction of random forest classifier. Figure 10: Confusion matrix on prediction of K-nearest neighbors (KNN) classifier. Then, we analyzed the impact of detected cyber threats on the parameters of the switching state (px4 io) and radio status of the UAVs. In particular, we analyzed the characteristics of the servo rail voltage and RSSI pin voltage for jamming, spoofing, and benign cases. Furthermore, we analyzed the parameters of the RSSI, remote RSSI, transmit buffer, noise, and remote noise effect on jamming, spoofing, and benign cases. Figure 15 shows the impact of the RSSI voltage on the switching states during jamming and spoofing attacks via the developed exploratory framework. A higher value for the RSSI voltage caused a powerful attack in UAV operation, since the control of the UAV was jammed by the attacker. Figure 16 shows the pair plot analysis between the attack and radio status signal. This correlation indicates the possibility of a strong attack in the received signal strength indicator and remote RSSI. Figure 16 also provides evidence that the attack takes place in the received signal strength indicator as well as the remote RSSI. Figure 14: Confusion matrix on prediction of LSTM. Figure 10.0 Confusion matrix on prediction of decision tree classifier. In our experiment, we first ran 50 epochs with a batch size of 75, and after that, we again ran 100 epochs with a batch sizes of 155 in KNN, logistic regression, random forests, decision trees, and LSTM for the training accuracy.