## 2 Related Works  
In the comparison, the Generative Adversarial Network (GAN) algorithm outperformed the Artificial Neural Network (ANN), Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), Decision Tree (DT), Convolutional Neural Networks (CNNs), and Auto Encoder techniques. The Generative Adversarial Network (GAN) method previously achieved a 99.34 percent classification accuracy [10]. The research described in [11] used machine learning and data mining techniques to combat network intrusion. The focus was on improving the intrusion detection rate while reducing false alarms. The paper [11] explores rule learning using RIPPER on imbalanced intrusion datasets and proposes a combination of oversampling, under-sampling, and clustering-based approaches to address the data imbalance. The evaluation of real-world intrusion datasets showed that oversampling through synthetic generation outperforms replication, and the clustering-based approach enhanced intrusion detection beyond synthetic generation alone. As the aforementioned article describes, an Intrusion Detection System (IDS) is a network security technology that monitors hostile activity on computers or networks. However, due to the dynamic  
Figure 1: The Intrusion Detection System. Figure 2: Network-Based Intrusion Detection System. and complicated nature of cyberattacks, typical IDS techniques require assistance. As a result, Ref. [11] proposes that effective adaptive methods, including machine learning approaches, can result in reduced false alarms, increased detection rates, and affordable computational costs. The performances of several methods based on conventional Artificial Intelligence (AI) and Computational Intelligence (CI) have previously been compared and reviewed. This has highlighted how the characteristics of CI techniques can be utilized to build effective IDSs [12]. [PERSON], [PERSON], [PERSON], [PERSON] [13] reviewed the classification accuracy of various classifiers using different dimensional features. [PERSON] and [PERSON], [PERSON] [14] proposed a novel approach that combines classification and techniques for the multiclass NSL-KDD Cup99 dataset. The proposed method utilizes Support Vector Machines (SVMs). [PERSON], [PERSON], [PERSON], [PERSON] [14] researched novel technologies to enhance the classification accuracy of Center and Nearest Neighbors (CANN) Intrusion Detection Systems. They evaluated the effectiveness of these technologies using the NSLKDD Cup99 dataset. In ref. [15], the authors discuss the hazards of hostile assaults on the network due to widespread internet use. They emphasize how crucial effective Intrusion Detection Systems are for categorizing and foreseeing cyberattacks. According to [15], a hybrid model that combines a firefly-based machine learning technique with Principal Component Analysis (PCA) can be used to categorize Intrusion Detection System (IDS) datasets. The XGBoost method uses hybrid PCA-firefly for dimensionality reduction in the model and classification and alters the dataset with One-Hot encoding. The proposed model outperformed the most advanced machine learning methods based on the experimental data. In ref. [16], the authors selected 55 features out of an initial 80, achieving a final accuracy of 95% by using the Deep Neural Network (DNN) and Binary Particle Swarm Optimization (BPSO) approaches and the classification method. [PERSON], [PERSON], [PERSON], [PERSON] [17] describe a machine learning-based NIDS model for binary classification of the CSECIC-IDS2018 using the AWS dataset. The abuse detection techniques of logistic regression, random forest, and gradient boosting were all implemented. However, we discovered that gradient boosting outperformed the others. The model's performance on the test dataset demonstrated its generalizability. Created using gradient boosting, the estimator exhibited a recall and precision of 0.98, making it suitable for practical applications.