## 2 Related Works/Literature Review  
[PERSON] et al. [16] developed an enhanced two-stage DL technique hybridizing long short-term memory (LSTM) and AE to detect attacks. To evaluate the parameters of the optimum network in the suggested model, CICIDS2017 and CICDS2018 were utilized. The results showed that the developed model performed better. [PERSON] et al. [17] developed two ML models combined with an extra tree (ET) classifier to detect network intrusion. Moreover, mutual information gain feature selection techniques were utilized for better accuracy. The UNSW-NB15 packet-based dataset was used to evaluate the performance of the suggested model. Compared to the current methods, the results proved that the developed model attained a higher accuracy of 96.24%, respectively. To identify varied classes of network intrusion, [PERSON] et al. [18] introduced a model named decision tree twin SVM and hierarchical clustering (HC-DTTWSVM). DT was created initially by utilizing a hierarchical clustering algorithm. Then, the bottom-up merging technique maximizes the distinction of DT's upper nodes. Twin SVM was incorporated in the generated DT to implement the suggested model. The performance evaluation was done using NSL-KDD and UNSW-NB15 datasets. The results demonstrated that the developed model could detect different classes of network intrusion and performed better than other techniques. [PERSON] et al. [19] developed an enhanced hybrid attention mechanism combined with an improved algorithm to enhance the capability for generation and accuracy. The feature information focuses on the essential characteristic data through the effective channel layer and curve space layer, enabling the model to focus more on the characteristics combined with classification and become more widely usable. Here, three datasets, namely CIIDS2017, UNSW-NB15, and CICDS2018, were utilized to verify the model's performance. The experimental results proved that the developed model attained higher accuracies of 100%, 99.7%, and 98.10% for binary classification issues and 96.37%, 98.12%, and 99.06% for multi-classification problems, respectively. To enhance the accuracy of IDSs, [PERSON] et al. [20] presented a customized CNN model. The outcomes of the suggested CNN model are compared with random forest (RF), a well-known ML method frequently applied in IDS. Using standard measurements like accuracy and f1-measure, the performances of both techniques were evaluated. Two datasets, namely UNSW-NB15 and CSE-CICIDS2018, were used to verify the efficiency of the suggested model. Compared to the RF algorithm, the proposed CNN model achieved better outcomes. According to experimental results, the developed model attained 99.17% accuracy in the UNSW-NB15 dataset and 99.18% for CSE-CICIDS2018. [PERSON] et al. [21] developed an enhanced NIDS by combining CNN and LSTM (NIDS-CNNLSTM) to classify intrusion detection. By combining the effective learning capabilities of LSTM neural networks and time series data, the suggested model learns and categorizes the features chosen by the CNN and validates its applicability using scenarios including binary and multi-classification. By utilizing the UNSW-NB15 and KDD-CUP99 datasets, the suggested models were trained. The final results proved that the developed model performed better than other current techniques and attained high classification accuracy and detection rates. Table 1 illustrates the comparisons from the literature survey.