## 5 Experiments and Results
### Analysis of XGBoost Predictions Using XAI Results  
To confirm that the outcomes from each of these techniques--XGBoost, SHAP, and LIME--are consistent, the study entails a number of crucial procedures. First, we compare the feature importance rankings provided by XGBoost, SHAP, and LIME to identify both common and distinct features across the methods. This comparison allows us to understand how each technique interprets and prioritizes features in relation to the model's predictions. Next, we focus on the features that are consistently recognized as important across all three methods--XGBoost, SHAP, and LIME. This consistency is essential for validating that these features are genuinely relevant and robust, particularly in addressing the intricate security challenges expected in 6G environments. We also cross-verify these key features across the methods to confirm their alignment and ensure the model's decision-making process remains consistent, a crucial step in achieving the transparency and reliability necessary for autonomous and complex 6G systems. Finally, we examine how these significant features affect the model's predictions and confirm that their impact is consistent across the differentmethods. This thorough analysis ensures that the model's predictions are interpretable, trustworthy, and capable of supporting critical security decisions in the highly dynamic and intelligent landscape of 6G networks. The following analysis can be made:  
* \"rst_count\" and \"IAT\" are highlighted by both XGBoost and SHAP as being extremely important features for the model's prediction.
* It is observed that the feature \"Protocol Type\" has the least impact. This can be a result of the information it offers being redundant or overlapping with other, more significant features. For example, flags such as \"ack_flag_number\", \"syn_count\", \"fin_count\", and \"urg_count\", which are highlighted as influential features, offer detailed insights into the behavior of TCP connections, often implicitly indicating the protocol in use, rendering \"Protocol Type\" feature as somewhat redundant. Moreover, features like \"Rate\" and \"Header_Length\" can indirectly capture protocol-related characteristics, while the inclusion of HTTPS/HTTP features further diminishes the standalone importance of \"Protocol Type\". Since these other features collectively capture the key characteristics that \"Protocol Type\" would convey, its contribution to the model's predictions becomes less significant.
* Both approaches consistently rank variables including \"flow_duration\", \"HTPS\", \"ack_flag_number\", \"Variance\", \"Header_Length\", \"Rate\", \"Std\", \"Max\", \"Magnitude\", \"Radius\", \"Duration\", and \"Covariance\" highly, demonstrating their significance. The trustworthiness of these features in the model is further supported by the overlap in feature priority rankings between XGBoost and SHAP. Additionally, by comparing the feature importance results from XGBoost and SHAP, we can be sure that both approaches concur on the key elements that influence the model's predictions, improving the decision-making process's dependability and transparency.
* Both approaches emphasize \"rst_count\", \"IAT\", and \"flow_duration\" as important aspects when comparing the predictions of the XGBoost model with the outcomes of LIME, highlighting their significance in the model's decision-making process. This widespread acknowledgment highlights the consistent function of these variables in traffic type prediction, whether using the global model perspective of XGBoost or the localized explanations of LIME.  
In conclusion, essential characteristics including \"rst_count\", \"IAT\", \"flow_duration\", \"Rate\", and \"Header_Length\" are consistently identified as significant in the comparison of XGBoost, SHAP, and LIME. This agreement amongst the approaches emphasizes how reliable and strong these traits are in influencing the model's predictions, underscoring their critical function in identifying and evaluating IoT threats.