## 2 Related Work  
Many researchers have investigated network attack detection and conducted experiments to enable network systems and users to communicate online in a stable and secure environment. The efficiency of existing IDS is assessed by accuracy, the detection rate, the false alarm rate, and number of features used. Selecting appropriate network features has a positive impact on improving ID performance. In 2016, researchers [15] developed a hybrid model for an IDS based on machine learning methods that operates by analyzing the behavior of network data and comparing it with the behavior of prior features. By reducing the number of network features to process from 41 to 29, this system achieved a detection accuracy of 88.23%. In 2019, researchers [16] applied a feature filtration method called \"wrapper\" that is based on the linear correlation coefficient algorithm (FGLCC) and the cuttlefish algorithm (CFA). This method uses a decision tree as the classifier. Experiments revealed that this method achieves an intrusion detection accuracy of 95.03% and a low FAR of 1.65%. In the same year, [PERSON] [17] proposed an IDS based on fuzzy logic and genetic network programming (GNP), which he abbreviated as FLGNP. The main role of the proposed system is features filtration. Consequently, this system selected 24 features to process out of the 41 input features. The test results revealed that this system had a detection accuracy of 94.8%. Researchers [8] have proposed group convolutional neural networks (CNNs) for filtering network features by finding correlations between features. Three steps were performed to accomplish this task. Initially, correlation coefficients were computed to identify and quantify the relationships between the features. To analyze the correlation coefficients, they were first sorted in descending order and grouped into columns. A one-dimensional convolutional neural network model (CNN) with convolution kernels and pooling filters was then used to process the grouped data. Subsequently, the reconstructed network features are fed into shadow machine learning models to predict network attacks. The results revealed that the features generated by the group CNN model could reduce the dimensionality of the data and maximize performance. Subsequently, a machine learning model that relied on the constructed features as inputs was applied. To evaluate the performance of this feature filtration method, experiments were conducted on the KDD99 and CICMalDroid2020 datasets, which were categorized as modest datasets. Data subsets corresponding to varying proportions of the original datasets were employed in the network feature filtration experiments using group CNNs. The results showed that the proposed feature filtration method performed better on the KDD99 dataset than on the CICMalDroid2020z dataset in terms of accuracy for all studied data proportions (subsets). The highest accuracy achieved by this system when applied to the KDD99 and CICMalDroid2020 datasets was 97.64% and 81.1%, respectively. [PERSON] [13] presented a cuckoo method for feature filtration (cuckoo-ID) to trim irrelevant features. Nineteen filtered features were used in the XCS. Experiments were conducted on the KDD99 dataset, and the results revealed that the DR of the proposed system was 98.9% and that the FAR was 0.09%. In 2022, [PERSON] [18] proposed an ID method that depended on feature learning and combined a CNN with a BiLSTM. This method was tested using the KDD99 dataset. Both CNN and BiLSTM were used to extract information on the features that influence the detection accuracy, rather than feature filtration. For intrusion classification, a C5.0 classifier was employed to improve the ID by increasing the DR and reducing the FAR. Experiments revealed that this method could increase the mean detection accuracy to 95.50% and reduce the false positive rate to 4.24%. In 2019, [PERSON] et al.