## 5 XAI Analysis for GNSS Signal Disruption Classification
### Local Explanations Results--LIME Technique
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{4}{c}{**(a)**} \\ \hline
**AI Model** & **Class** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{6}{*}{DT} & MCWI & 100 & 100 & 100 \\  & MP & 100 & 97 & 99 \\  & Chirp & 19 & 23 & 20 \\ \cline{1-1}  & Clean & 3 & 3 & 3 \\  & CWI & 100 & 100 & 100 \\  & Pulse & 100 & 100 & 100 \\  & Spoofing & 98 & 100 & 99 \\  & MCWI & 100 & 100 & 100 \\  & MP & 100 & 97 & 99 \\  & Chirp & 80 & 70 & 80 \\ RF & Clean & 16 & 17 & 17 \\  & CWI & 100 & 100 & 100 \\  & Pulse & 100 & 100 & 100 \\  & Spoofing & 98 & 100 & 99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: (**a**): Results of ML models’ prediction performances using frequency domain feature selection method on GNSS dataset under k = 6. (**b**): Results of ML models’ prediction performances using time domain feature selection method on GNSS dataset under k = 12. Table 2b gives a quantitative assessment of the ML model performance on LIME, which is essential for comparing the effectiveness across various scenarios. When it comes to the time domain features, the DT and RF models performed exceptionally well; in fact, the robustness and reliability of the model results can be seen in several cases with a \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & & \multicolumn{3}{c}{**(a)**} \\ \hline
**AI Model** & **Class** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{KNN} & MCWI & 100 & 100 & 100 \\  & MP & 100 & 97 & 99 \\  & Chirp & 31 & 35 & 33 \\  & Clean & 26 & 23 & 24 \\  & CWI & 100 & 100 & 100 \\  & Pulse & 100 & 100 & 100 \\  & Spoofing & 98 & 100 & 99 \\  & MCWI & 91 & 100 & 95 \\  & MP & 100 & 97 & 99 \\  & Chirp & 50 & 68 & 57 \\  & Clean & 38 & 33 & 35 \\  & CWI & 100 & 72 & 84 \\  & Pulse & 100 & 97 & 99 \\  & Spoofing & 98 & 100 & 99 \\  & MCWI & 0 & 0 & 0 \\  & MP & 0 & 0 & 0 \\  & Chirp & 50 & 100 & 67 \\ AdaBoost & Clean & 0 & 0 & 0 \\  & CWI & 100 & 100 & 100 \\  & Pulse & 50 & 100 & 67 \\  & Spoofing & 0 & 0 & 0 \\ \hline \hline \multicolumn{4}{c}{(b)} \\ \hline
**AI Model** & **Class** & **Precision** & **Recall** & **F1-score** \\ \hline \multirow{8}{*}{DT} & MCWI & 100 & 99 & 100 \\  & MP & 99 & 100 & 99 \\  & Chirp & 100 & 100 & 100 \\  & Clean & 100 & 100 & 100 \\  & CWI & 100 & 100 & 100 \\  & Pulse & 100 & 100 & 100 \\  & Spoofing & 100 & 100 & 100 \\  & MCWI & 100 & 100 & 100 \\  & MP & 100 & 97 & 99 \\  & Chirp & 80 & 70 & 80 \\ RF & Clean & 16 & 17 & 17 \\  & CWI & 100 & 100 & 100 \\  & Pulse & 100 & 100 & 100 \\  & Spoofing & 98 & 100 & 99 \\  & MCWI & 34 & 39 & 36 \\  & MP & 27 & 43 & 33 \\  & Chirp & 40 & 42 & 41 \\ KNN & Clean & 26 & 25 & 26 \\  & CWI & 99 & 91 & 95 \\  & Pulse & 42 & 31 & 36 \\  & Spoofing & 30 & 17 & 22 \\  & MCWI & 0 & 0 & 0 \\  & MP & 32 & 4 & 8 \\  & Chirp & 0 & 0 & 0 \\ SVM & Clean & 28 & 6 & 9 \\  & CWI & 100 & 72 & 84 \\  & Pulse & 6 & 1 & 1 \\  & Spoofing & 98 & 100 & 99 \\  & MCWI & 33 & 99 & 50 \\  & MP & 0 & 0 & 0 \\  & Chirp & 4 & 12 & 14 \\ AdaBoost & Clean & 0 & 0 & 0 \\  & CWI & 100 & 100 & 100 \\  & Pulse & 100 & 100 & 100 \\  & Spoofing & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._perfect score of 100%. However, looking into the KNN and SVM models showed significant variability in some conditions; they also achieved very low accuracy, recall, and F1-score values. AdaBoost completely failed to provide the correct signal prediction for the MP and spoofing signals but performed well in other categories.