## 5 XAI Analysis for GNSS Signal Disruption Classification
### Local Explanations Results--SHAP Technique
For the time domain features, the mean, median, and maximum-to-mean ratio also do not contribute to the model's prediction. A comparison of all models in both the time and frequency domains is depicted in Figure 9a,b. The mean, median, 75 th percentile and maximum-to-mean ratio are the least important features, and most of the attributes for ML models in the time domain are not utilized to predict the output. The mean absolute deviation is the only feature used in KNN. In the frequency domain, the normalized spectrum kurtosis is used by most of the ML models, as shown earlier in the feature importance plot; also, the ratio of the maximum peak to the second maximum peak of the normalized spectrum has not been considered much in forecasting the results of the model. In this work, initially, the global SHAP values for all ML models are calculated; then, the feature importance is computed by multiplying the SHAP value with the model accuracy for each feature. Finally, we determine the overall average ranking across models. The frequency domain feature values are chosen from the initial value for the top k features. The metrics of all ML models show that a suitable performance can be obtained by selecting only the top four features and removing two unimportant features, resulting in the same performance, as can be seen in Table 4a. There is not much improvement in choosing more than four attributes; therefore, the last two features did not considerably improve the model performance. Similarly, the time domain features are also used to evaluate the model's performance, and there is no change in the accuracy value after increasing the feature values beyond K = 6; the RF model gives a good performance compared to the other models. Table 4b shows the effectiveness of top features in classifying the types of disruption. For the most important features, using traditional feature engineering, the model behaviour is not interpreted with respect to the types of disruption. The features are ordered by their importance for each category of jamming instances using the summary plot in order to jointly analyze the feature importance and feature effects. Each point represents a Shapley value for a feature and an instance. The position on  
Figure 9: (**a**): Frequency domain feature importance comparison using different ML algorithms. (**b**): Time domain feature importance comparison using different ML algorithms. the y-axis corresponds to the attributes, and the x-axis specifies the Shapley value, with the colours indicating the feature values from low (blue) to high (red). A positive value pushes the prediction value higher, and negative values lower the prediction. The summary plots in Figure 10a show that the mean absolute deviation, being the least important feature, has low Shapley values for CWI and chirp interference. For MP and spoofing, the following features have no impact: the mean, mean absolute deviation, maximum-to-mean ratio, median, and skewness. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & & & & & & \\ \hline
**No. of Features** & **Metric** & **DT** & **RF** & **KNN** & **SVM** & **AdaBoost** \\ \hline \multirow{4}{*}{K = 1} & Accuracy & 0.4500 & 0.2857 & 0.4750 & 0.4607 & 0.4223 \\  & Precision & 0.4524 & 0.1667 & 0.5125 & 0.3794 & 0.3901 \\  & Recall  
In Figure 10b, the summary plot illustrates that the least significant feature is the RMPS, with low Shapley values, for the CWI and spoofing categories. The chirp interference summary plot indicates that single-frequency energy aggregation and normalized spectrum kurtosis have a more neutral effect on the model's predictions, and features like the normalized spectrum bandwidth and the ratio of the variance to the squared mean of the normalized spectrum have a significant impact. The dispersion of points also shows how different features have an impact on various cases. The outcome of a SHAP model with a clear explanation for a specific GNSS signal type is visualized in Figure 11a,b in the form of a force plot.