## 2 Related Work
### Feature Extraction Techniques with Classification Algorithms  
Feature extraction involves constructing new features from existing features, a strategy explored in numerous studies. [PERSON] et al. (2018) and [PERSON] et al. (2019) proposed autoencoder algorithms to develop features from datasets. [PERSON] et al. (2018) employed a Triplet Convolutional Neural Network for classifying intrusion data, tested using the CICIDS2017 and KDD99 datasets. [PERSON] et al. (2019) utilized a Naive Bayes classifier with the NSL-KDD dataset. [PERSON] et al. (2019) utilized autoencoder deep learning techniques with LSTM for binary classification using the UNSW-NB15 dataset. Another innovative approach was presented by [PERSON] et al. (2019), who introduced a hybrid autoencoder and decision tree algorithm. This approach involves training the autoencoder on positive sample data to align its parameters with normal data flow. By calculating the loss value, discrepancies between normal and abnormal samples are identified and transformed into standardized features. This method prevents information loss due to high-dimensional data reduction, ensuring speed and accuracy. Using an autoencoder in conjunction with a decision tree for intrusion detection demonstrates greater efficiency compared to using a decision tree alone or other traditional machine learning techniques. In a distinct approach, [PERSON] (2019) and [PERSON] et al. (2019) proposed effective IDSs using PCA for feature extraction. [PERSON] (2019) applied a polylogarithmic-function-based Naive Bayes classification technique, while [PERSON] et al. (2019) employed random forest classification. Both studies were evaluated on the KDD99 dataset, highlighting the versatility of PCA in enhancing IDSs. The authors of ([PERSON] et al., 2020) utilized the NSL-KDD dataset to assess their proposed models, addressing both binary and multiclass classification scenarios. Their study employed a CNN to extract relevant features from the input data, and these features were subsequently passed into an LSTM for the sequence analysis. In another study, [PERSON] et al. (2020) introduced a novel intrusion detection technique called DBN-LSSVM, which combines a DBN with an LSSVM. Initially, the network dataset was pre-processed, and DBN was used to reduce the feature dimensionality. Then, the input weights and biases of LSSVM were optimized using the PSO algorithm to establish an effective intrusion detection model. Simulation experiments validated the proposed method on the KDD99 dataset. Table 1 summarizes prior research focusing on dimensionality reduction techniques and classification. Our analysis of previous studies highlights notable gaps in the existing literature. First, a significant portion of past research relies on outdated datasets that fail to encompass current network attacks. Second, the predominant focus on individual, boosting, or bagging algorithms in the literature often limits improvements in accuracy. Third, when researchers employ extensive datasets like CICIDS2017, they frequently concentrate on a narrow spectrum of common network attacks, neglecting rarer ones. As a result, their IDS solutions may lack robustness in identifying all potential cyber threats. Finally, there is a notable scarcity of studies aimed at developing and evaluating IDSs for multiclass prediction across multiple datasets that encompass diverse attack types and dataset sizes. To address the aforementioned issues, this work proposes a new scheme that utilizes dimensionality reduction to effectively eliminate the irrelevant and redundant features and also decrease the high dimensions. Additionally, we introduce a novel stacking ensemble technique, called multi-stacking ensemble, which efficiently enhances the identification capacity for a wide range of attack types. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author(s)** & **DR Techniques** & \begin{tabular}{c} **DR Technique** \\ **Type** \\ \end{tabular}  &