## 4 Results and Discussion
### Comparison with State-of-the-Art Methods  
In this section, the result of the EDRL model for attack classification is compared with advanced state-of-the-art approaches to determine its unique strength for intrusion detection. From the literature, the state-of-the-art approaches like SSDDQN [28], deep  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{5}{c}{**CICDS2017**} & \multicolumn{5}{c}{**NSL-KDD**} \\ \cline{2-10}  & **RF** & **CNN** & **GRU** & **DQN** & **Proposed** & **RF** & **CNN** & **GRU** & **DQN** & **Proposed** \\ \hline Accuracy \% & 97.67 & 97.35 & 97.83 & 98.89 & 99.93 & 96.26 & 96.67 & 96.78 & 96.85 & 99.42 \\ Precision \% & 97.76 & 97.47 & 98.1 & 98.56 & 99.78 & 96.89 & 96.24 & 96.68 & 96.89 & 99.32 \\ Recall \% & 97.78 & 97.59 & 97.89 & 98.65 & 99.57 & 97.12 & 97.01 & 96.98 & 97.32 & 99.12 \\ F1-score \% & 97.46 & 97.34 & 97.56 & 98.19 & 99.49 & 96.23 & 96.78 & 96.98 & 97.45 & 99.15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of the proposed and existing works using CICIDS2017 and NSL-KDD.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**K Fold Size**} \\ \cline{2-5}  & **10** & **15** & **20** & **25** & **30** \\ \hline RF & 0.7591 & 0.8094 & 0.8369 & 0.8277 & 0.8481 \\ CNN & 0.7486 & 0.7932 & 0.7793 & 0.8085 & 0.8191 \\ GRU & 0.7299 & 0.7451 & 0.7685 & 0.7978 & 0.7956 \\ DQN & 0.7268 & 0.7363 & 0.7359 & 0.7855 & 0.8150 \\ Proposed & 0.8086 & 0.8411 & 0.8486 & 0.8578 & 0.8981 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Achieved K-fold cross-validation value in terms of accuracy for both EDRL and existing models.  
SARSA-based RL [29], GRU-GWO [30], LSTM-GWO [30], and CNN+BiLSTM [31] are considered. Other state-of-the-art models such as micro reinforcement learning architecture (MRLC) [40], DRL with CNN (Soft DQN) (SDQN)) [41], deformable vision transformer (DE-ViT) [42], and conditional tabular generative adversarial network (CTGAN) [43] are also included to determine the ability of the EDRL model. An IDS based on MRLC [40] is presented to improve IDS accuracy by making use of a fine-grained learning framework. In [41], anomaly behavior in network systems is analyzed through the use of CNN architecture and DRL. The created system can generate patterns suitable for categorization using a large number of informative data and the Markov decision technique. Two viewpoints are used to analyze DQL: Soft DQL (SDQL), Double DQL, and Soft double DQL. Among the examined models, SDQL has attained maximum accuracy. In DE-VIT [42], a deformable attention mechanism module is included in order to focus on relevant areas, capture more valuable features, and prevent excessive memory and processing costs. In [43], a CTGAN has been employed in order to solve the problem of data imbalance in the dataset. Notable gains in detecting network intrusion are attained by training three shallow binary classification algorithms (logistic regression, decision trees, and Gaussian naive Bayes) on both the original imbalanced dataset and the CTGAN-balanced data. In particular, CTGAN uses a final XGBoost meta-classifier by using a new two-stage label-wise ensembling process. Table 7 displays the evaluated results of EDRL and state-of-the-art approaches in terms of accuracy for detecting intrusion in the network. In the tabular representation, it is perceived that the EDRL model has reached better results in attack classification than the state-of-the-art approaches due to the capability of DAE and IFSA that support the DQN model.