## 4 Results
### Evaluation Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Normal** & **DoS** & **Port Scan** & **Bot** \\
2 & **Bwd Packet Length Mean** & **D**estation Port** & **Packet Length Mean** & **Destination Port** \\
3 & **Total Length of Bwd Packets** & **Init\_Win\_Bytes\_Bwd** & **Init\_Win\_Bytes\_Fwd** & **Init\_Win\_Bytes\_Bwd** \\
4 & **Packet Length Std** & **Total Length of Bwd Packets** & **Total Length Fwd Packets** & **Packet Length Std** \\
5 & **Init\_Win\_Bytes\_Fwd** & **Bwd Packet Length Max** & **Average Packet Size** & **Packet Length Mean** \\ \hline
**Feature** & **Web Attack** & **Brute Force** & **Infiltration** \\
**Rank** & & **Destination Port** & **Destination Port** \\ \hline
1 & **Destination Port** & **Destination Port** & **Destination Port** \\
2 & **Init\_Win\_Bytes\_Bwd** & **Init\_Win\_Bytes\_Bwd** & **Init\_Win\_Bytes\_Bwd** \\
3 & **Init\_Win\_bytes\_Fwd** & **Init\_Win\_bytes\_Fwd** & **Init\_Win\_bytes\_Fwd** \\
4 & **Bwd Packet Length Mean** & **Total Length of Fwd Packets** & **Bwd Packet Length Mean** \\
5 & **Packed Length Std** & **Total Length of Bwd packets** & **Total Length of Fwd Packets** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The feature importance (given by the top five features) for each attack type for the CICIDS-2017 dataset. The feature _Destination Port_ is the common top feature across the different attack types. \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Feature** & **Normal** & **DoS** & **Probe** \\
1 & **dst\_host\_srv\_count** & **dst\_host\_serror\_rate** & **dst\_host\_serror\_rate** \\
2 & **src\_bytes** & **diff\_srv\_rate** & **dst\_host\_same\_src\_port\_rate** \\
3 & **dst\_host\_same\_src\_port\_rate** & **flag\_S0** & **dst\_host\_same\_srv\_rate** \\
4 & **service\_http** & **serror\_rate** & **src\_bytes** \\
5 & **hot** & **same\_srv\_rate** & **dst\_bytes** \\ \hline
**Feature** & **R2L** & **U2R** & **U2R** \\ \hline
1 & **count** & **dst\_host\_count** & **dst\_host\_count** \\
2 & **dst\_host\_same\_src\_port\_rate** & **dst\_host\_ssrv\_count** & \\
3 & **dst\_bytes** & **dst\_host\_same\_src\_port\_rate** & \\
4 & **hot** & **hot** & **hot** \\
5 & **dst\_host\_count** & **count** & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The feature importance (top five features) for each attack type for the NSL-KDD dataset. The features _dst\_host\_same\_src\_port\_rate_, _hot_, and _dst\_host\_count_ are common top features across different attack types. Runtime Analysis:We now present our runtime analysis. 1. [label=()]
2. **AI Model Training Runtime:** For the CICIDS-2017 dataset, Table 14 shows that training the AI models on millions of samples takes, at most, a few minutes for most of the AI models considered in our work. It also shows that selecting all features for training would lead to a longer training time compared to only selecting the top 15 features (2.1X average increase across all AI models). It also shows that KNN and RF have the fastest training runtimes, respectively. On the other hand, LightGBM has the slowest training runtime for this dataset. For the RoEduNet-SIMARGL2021 dataset, Table 14 indicates that KNN and DNN exhibit the shortest training runtimes, whereas SVM demonstrates the longest training runtime. Similarly, for the NSL-KDD dataset, Table 14 reveals that KNN consistently has the fastest training times regardless of feature selection. In contrast, SVM has the most extended training duration, particularly for 15 features, which takes 6.48 min. On the other hand, LightGBM's training is relatively efficient, taking around  
to generate local explanations. For SHAP, the RF, DNN, and LightGBM models incur very little time to generate global explanations, irrespective of the number of samples for such explanations. On the other hand, the ADA and KNN models are not scalable to a larger number of samples. To test LIME globally, we tested generating LIME explanations for one thousand samples.