## 4 Results and Discussion
### Simulation Scenario  
The proposed EDRL model includes different sequential phases to achieve classification and policy generation. Initially, pre-processing is performed to enhance the data for further analysis. The enhancement of DQL is carried out based on deep AE and IFSA. Three primary parts make up every DRL model, particularly actions, states, and Q-values. In the proposed EDRL model, the pre-processed data are characterized by state. When it comes to labeling, an action resembles providing a label to the data, like finding whether an instance is an attack or not. In this circumstance, the probability related to the label is enclosed in the Q-value. Training an AE with labeled data is the first step in the labeling process. The Q-network is then used to implement this AE. Within the Q-network, the role of AE is to predict the Q-values (under supervision), updating its weights and biases depending on the features that are extracted. The network can label unknown data (in an unsupervised manner) due to this update process. Inherently, the AE assists the Q-network in identifying and predicting labels for new data points by learning the labeled data it trained initially. A crucial Q-learning parameter that affects the trade-off between exploration and exploitation is optimally updated throughout the learning process using IFSO. By optimizing the parameter, the proposed EDRL model helps improve its performance and learning efficiency. The complete implementations of the work are done in the Python platform, and the system specification followed is presented in Table 1. The system is equipped with an Intel(r) CoreTM i5-3470 CPU operating at a clock speed of @3.20 GHz, featuring 4 Cores and 4 Logical Pro, building it appropriate for handling moderate computational tasks effectively. It encompasses 8 GB of RAM, with 7.85 GB usable for running resource-intensive applications like IDS simulation. The system runs on Microsoft Windows 10 Pro (version 10.0.19044 Build 19044), a 64-bit operating system that assists modern software and makes compatibility with different tools. The architecture is an x64-based processor, optimizing performance for applications designed for 64-bit systems. Several hyperparameters are tuned initially to perform the implementations in the proposed work as different algorithms are utilized. The hyperparameter tuning of the proposed work is presented in Table 2. By using deep AE for efficiently approximating the Q-function, four encoder and decoder layers would be an appropriate hyperparameter configuration. This permits the capturing of complex feature representations while ensuring precise reconstruction. The batch size is set to 64 for balance training stability and efficacy, and the model is trained for 100 epochs to permit adequate learning without overfitting. The ReLU activation function for the hidden layers encourages the learning of non-linear correlations, while a linear activation function in the output layer is suitable for forecasting continuous Q-values. By efficiently approximating the Q-function, this configuration permits the AE to acquire significant representations of the state-action space, enhancing performance and generalization in reinforcement learning tasks. When the data are linearly separable, it is better to use a linear kernel function in SVM for attack classification. This lowers computational complexity and simplifies the model, resulting in faster inference and training while preserving interpretability and efficacy in differentiating between normal and attack classes. Also, the gamma parameter is set to 0.25, and it supports shaping the decision boundary of SVM. Considering the FSA, the initial population is set to 50, and the maximum number of iterations is 100. A diversified representation of potential solutions is made by setting the initial population to 50, which is indispensable for efficient search space exploration. Typically, this size is satisfactory for capturing various parts of the solution landscape without overwhelming computational resources.