## 1 Introduction  
Cybersecurity and security against frequent cyber-attacks have become critical issues in recent times. The leading cause of cyber-attacks is an enormous expansion in computer networks and the number of significant applications that individuals utilize for private or business use. It causes more monetary losses and damage in large-scale networks [1]. The challenges caused by future requirements cannot be handled by the current solutions, which include user authentication, hardware and software firewalls, and data encryption techniques. Unfortunately, it cannot protect computer networks from numerous cyber threats. These traditional security mechanisms cannot defend against intrusions due to the intrusion systems' faster and more demanding expansion [2; 3]. Therefore, to generate more precise detection techniques, researchers have been developing intrusion detection for cyber-attacks. A dynamically monitored environment, such as system calls or syslog records and network traffic of operating methods is known as an intrusion detection system (IDS). Its primary function is to identify whether the actions observed indicate an attack or lawful use [4; 5]. The IDS can be integrated into two phases, namely, host and network-based intrusion detection systems (HNIDS). A HIDS tracks system information, while a NIDS operates on characteristic vectors that include compiled data about network traffic over an established time [6]. However, many advances are present in network topologies, hardware, and software, such as cyber-attacks and the Internet of Things (IoT), which are becoming more challenging to detect. Due to the variable nature of cyber-attacks,anomaly detection is a captivating field for the utilization of more conventional classifiers like K-nearest neighbors (KNN), support vector machines (SVM), and random forests (RF). However, to evaluate and compare various anomaly detection techniques, extensive and accurate data from simulated network environments are needed [7]. The existing literature has identified that one of the main issues in intrusion detection research is the need for trustworthy datasets. For better detection, deep learning (DL) has been employed. Among the DL models, recurrent neural networks (RNN) and convolutional neural networks (CNN) were mainly utilized to minimize the complexity of the current classifiers. Moreover, DL networks are well-known for their enhanced performance, so they can be used as a central component in network intrusion systems [8; 9]. However, these models are costly and require more time to train the data. Likewise, artificial neural networks (ANN) were also utilized in IDS [10]. Due to its numerous benefits, an ANN is particularly well-suited for network intrusion detection. Initially, with an enormous amount of input characteristics, the model performed better in demonstrating non-linear data [11]. Next, when trained thoroughly, an ANN will quickly detect cyber-attacks. An ANN model is trained using a large dataset developed into a generalized solution for a specific task. On the other hand, intrusion detection is the conventional signature-based IDS that relies on manually established, accessible rules [12]. Similarly, auto-encoders (AE) have also been employed in IDS. Combining generative adversarial nets (GAN) into the AE, an enhanced method named adversarial auto-encoders (AAE) was utilized to detect cyber-attacks. However, this model had overfitting issues and a high computational load [13]. To generate computational techniques for detecting different types of attacks, varied incident patterns need to be analyzed before using cybersecurity data to estimate potential threats. Data-driven intelligent intrusion detection is the name assigned to it [14; 15]. The primary objectives of this research are as follows:  
To minimize the effect of maximum or minimum value on overall features, original data is first normalized using the min-max method. To balance the classes, use SMOTE techniques, which help reduce overfitting issues.