## 4 Results and Discussion
### Discussion  
The proposed EDRL model in this research combines the benefits of deep AE and IFSA for enhancing normal and attack detection. The deep AE in DRL is a powerful tool for extracting features and minimizing dimensionality. It permits the model to learn complex \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Dataset** & **Accuracy (\%)** \\ \hline SSDDQN [28] & NSL-KDD & 83.1 \\ \hline \multirow{2}{*}{Deep SARSA-based RL [29]} & UNSW-NB15 & 82.62 \\  & NSL-KDD & 84.36 \\ \hline \multirow{2}{*}{GRU-GWO [30]} & UNSW-NB15 & 90 \\  & NSL-KDD & 94 \\ \hline \multirow{2}{*}{LSTM-GWO [30]} & UNSW-NB15 & 93 \\  & NSL-KDD & 94 \\ \hline CNN+BiLSTM [31] & UNSW-NB15 & 97.9 \\ \hline \multirow{2}{*}{MRLC [40]} & UNSW-NB15 & 98.97 \\  & CICIDS2017 & 99.92 \\  & NSL-KDD & 98.99 \\ \hline SDQN [41] & UNSW-NB15 & 99.24 \\ \hline \multirow{2}{*}{DE-ViT [42]} & UNSW-NB15 & 97.5 \\  & CICIDS2017 & 99.5 \\ \hline \multirow{2}{*}{CTGAN [43]} & UNSW-NB15 & 98 \\ \hline \multirow{2}{*}{Proposed} & UNSW-NB15 & 99.6 \\  & CICIDS2017 & 99.93 \\ \cline{1-1}  & NSL-KDD & 99.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the proposed EDRL with state-of-the-art approaches. patterns in both labeled and unlabelled data with no need for extensive labeling. Through the reconstruction of input traffic features, the deep AE captures the structure of normal behavior, which is vital to distinguish normal and malicious activities. Meanwhile, IFSA facilitates optimal policy selection by optimizing Q-function. This enables the model to adaptively adjust its decision-making process depending on the learned representation for AE. This not only enhances the model's capability to detect attacks but also improves the robustness in handling dynamic and diverse network environments. Moreover, the proposed EDRL's semi-supervised methodology enables it to use a wider data range, reducing the requirement for labeled instances while upholding good detection performance. This makes it especially appropriate for real-world applications where labeled data may be costly or scarce to obtain. Despite its effectiveness, the proposed EDRL model has drawbacks with regard to scalability and training time when used on larger datasets because of the deep AE's complexity, and the optimization process using the IFSA can result in higher computational demands. In addition, there are difficulties in adapting the model for real-time intrusion detection, comprising the necessity for quick decision-making and the capability to handle streams of data with high velocity. However, effective feature extraction through the incorporation of AE lowers the dimensionality of the input data and speeds up inference and training. Scalability and real-time application issues can be effectively addressed by adjusting the IFSA's optimization capabilities to improve the model's responsiveness and maintain high performance even in dynamic circumstances. As an advanced layer for threat classification and anomaly detection, the EDRL model can be easily included in current security infrastructures. Because of the potential to learn from both labeled and unlabelled data, it can adapt to varying threat landscapes and uninterruptedly improve its detection capabilities when new attack patterns appear. In low-resource environments, like Internet of Things (IoT) networks, the effective feature extraction ability of the model using the deep AE lowers computational overhead, permitting it to function well on devices with constrained processing power. In addition, the framework is ideal for implementation in a variety of environments since the IFSA's optimization may be adjusted to highlight resource conservation while retaining detection accuracy. In addition to enhancing an organization's security posture, this flexibility upsurges the model's applicability across different sectors with distinct threat detection challenges. Furthermore, the proposed EDRL model improves adaptability to hidden or complex attacks by learning generalized feature representations.