## 1 Introduction  
Internet usage and growing technologies raise the risk of cyber security installments on cloud computing, edge computing, and other networks. These malicious attacks lead to financial and reputational losses. However, the network intrusion detection system (NIDS) installed on these networks prevents these cyber-attacks [1, 2, 3]. NIDS basically monitor the network continuously within its cyberspace [4]. The history of intrusion detection systems starts at 1980 where [PERSON] et al. proposed a method [5] that was enough to secure a network at that time with required security. However, the immense progress in technologies in recent decades has created many challenges regarding network security. The big data created by technologies nowadays makes millions of gigabytes (GBs) of data that are shared across the network nodes. The network itself becomes larger in size and it also challenging to maintain the network safely. Network security also becomes more challenging when certain types of cyber-attacks come around. The false rate of NIDS increased with the arrival of these different types of attacks [6]. Therefore, it has become a worldwide issue to make such types of NIDS that not only detect the attack accurately and precisely, but also detect the attack type to tackle it correspondingly. Although these are the specific challenges of NIDS that need to be solved, certain challenges also occur while the NIDS are proposed. NIDS typically made using machine [7, 8] and deep-learning [9, 10] intelligent methods. However, to make these models, the network-logs-based generated data are used to train and test the models. The class imbalance problem is very common in these datasets. The class imbalance occurs when the ratio of certain categories, such as positive class, is increased as compared to another class or negative class [11, 12]. It increases the prediction probability of trained ML and Deep Learning (DL) models being more likely toward the class that contains more instances in a given dataset.This class imbalance ratio reached 1:50%+ in the UNSW-NB15 dataset against each category [13] as compared to the normal class. The dataset used in NIDS is mostly the remaining class imbalance risen to compromise the previously proposed NIDS with a higher false rate of NIDS [14]. Further, the class imbalance issue almost remains unsolved while any NIDS is proposed. However, few studies have solved this class imbalance issue and proposed their frameworks of NIDS. These proposed NIDS still need improvement in terms of improved precision rate and use the robust method to solve the problem of class imbalance [15, 16]. The class imbalance problem is solved mainly on two levels [17]. It could be solved using the data level scheme that focuses on class balancing using the class distribution methods, such as the synthetic minority oversampling technique (SMOTE) [18]. The other method to solve class imbalance is algorithm-based solutions. In this method, the cost metric uses a cost-sensitive function to solve misclassified instances on validated data. The neural network is a simple example of this that uses a cost-calculating method to assign reweighing to misclassified instances [19]. In this way, the algorithm reaches a certain condition such that it removes the misclassification behavior of misclassified instances and makes a balanced dataset. The other problem after the data disbalancing is the overfitting of data, which is the problem for ML and DL models. The overfitting could be reduced using appropriate features selection that ultimately leads to the performance enhancement of NIDS models. The wrapper, embedded, and filter are a few famous approaches that are used for feature-selection purposes. However, ML and evolutionary computing also provided a few methods that were used as feature selection methods, such as genetic algorithm (GA), particle swarm optimization (PSO), etc. [20]. However, these applied methods ultimately aimed to improve the model performance, which was validated by their proposed studies.