## 5 XAI Analysis for GNSS Signal Disruption Classification
### Local Explanations Results--LIME Technique  
The prediction outputs of various ML algorithms are analyzed using the LIME model, which generates the local explanations by approximating the ML model's output with an interpretable result based on the class prediction probabilities. The explanations for the predictions of the DT, KNN, AdaBoost, RF, and SVM models using the frequency domain features are shown in Figure 7. For a specific sample in the test dataset, the model correctly predicted the class as spoofing, as the most important features for this sample are 'normalized spectrum kurtosis' and'single-frequency energy aggregation' have more positive values that are the reason why the model correctly predicted the sample as spoofing. From the results, it is evident that the total value of features confirming that it is a spoofing category is greater than that of non-spoofing features. Therefore, the model correctly predicted this sample as spoofing. On the other hand, using the AdaBoost algorithm, the prediction probability of the testing instance appears to be 33% for MP and MCWI; this ambiguity led to inaccurate results despite the sample belonging to the spoofing category. The performance metrics of ML models using the LIME framework are given in Table 1. Among all models, the SVM has the highest prediction results, and AdaBoost showed poor performance. Figure 6: The proposed XAI framework-based GNSS signal disruption classification model. Figure 7: _Cont._  
Figure 7: (**a**) Frequency domain LIME model decision for correct/incorrect predictions of GNSS signal for different ML models (**i**) Decision tree, (**ii**) AdaBoost, (**iii**) KNN, (**iv**) Random forest, and (**v**) SVM. (**b**) Time domain LIME model decision of correct/incorrect predictions of GNSS signal for different ML models: (**i**) Decision tree, (**ii**) AdaBoost, (**iii**) KNN, (**iv**) Random forest, and (**v**) SVM. The prediction results of choosing all 12 features in the time domain using LIME are shown in Figure 7b. The RF model has the highest prediction accuracy of 71%, followed by DT and SVM. These models correctly predicted the class as MCWI, whereas other ML models could not be able to give the correct prediction. However, compared to frequency domain attributes, the performance of the ML model is significantly lower because most of the time, domain characteristics of the signal make it very difficult to distinguish the temporal relationships across the categories such as the multipath, spoofing, and clean signals, as these signal characteristics are highly similar in the time domain. Table 2a shows the ML models' performances based on the predicted label on the nth instance for the corresponding test sample using all six features in the dataset using the LIME algorithm. Here, there is difficulty in predicting the clean signal, as can be seen from the outputs of all the ML models. The AdaBoost model could not predict the MP, MCWI, clean, and spoofing signals correctly and failed in predicting all instances. RF has superior performance in predicting all kinds of interference, except chirp. Compared to all models, the SVM provided better metrics. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{4}{c}{**(a)**} \\ \hline
**AI Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline DT & 74 & 74 & 75 & 74 \\ RF & 75 & 75 & 75 & 75 \\ KNN & 79 & 79 & 79 & 79 \\ SVM & 81 & 82 & 81 & 81 \\ AdaBoost & 29 & 29 & 43 & 33 \\ \hline \multicolumn{4}{c}{(b)} \\ \hline
**AI Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline DT & 66 & 65 & 64 & 65 \\ RF & 71 & 73 & 72 & 73 \\ KNN & 41 & 41 & 41 & 43 \\ SVM & 16 & 12 & 16 & 6 \\ AdaBoost & 57 & 48 & 57 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: (**a**) Overall performance of the LIME model in the frequency domain using top six features. (**b**) Overall performance of LIME model on time domain using top 12 features.