## 2 Literature Survey  
With the massive increase in network traffic data, learning-based network intrusion detection systems (NIDS) have been introduced to eradicate different unauthentic network actions and possible hidden threats [13]. However, various issues exist in the design and implementation of the learning-based NIDS. (1) Standard network intrusion discovery approaches frequently used several ways such as dimension reduction, compression, and filtering approaches to reduce measurement noise to tackle the intricacy of dealing with large-scale, high-dimensional data facts. Consequently, extracting features for incursion behaviors will likely eliminate concealed but essential information. Hence, it might result in high false discovery rates. (2) For learning-based NIDS to accurately identify the aspects of intrusion activities, a significant volume of labelled data samples is often needed. The amount and quality of the labelled training set significantly impact how well the NIDS performs. Generating superior, significant training instances is challenging because we traditionally label the training samples manually, which is a time-consuming and error-prone method. (3) NIDS must react to intrusion behaviors in the real world to minimize the loss under an attack. For instance, overflow attacks are frequently concealed in network traffic that can get past the firewall. If such assaults are not quickly identified and stopped, the perpetrator may use them as a Launchpad to transmit a flood of negative posts to the intranet and leave a backdoor open in the breached system. (4) Class imbalance data lead to misclassification, which consists of minimal minority classes and huge majority classes. Hence, class imbalance requires optimization and parallelization architecture for a real-time intrusion detection system [14]. The deep learning model has attracted researchers and industry personnel to handle complex problems. It has been given significant importance in research on cyber security to improve the research quality [15]. [PERSON] et al. [9] proposed an RNN-based IDS to classify multi-class intrusion on the NSL-KDD dataset. They tested the model by varying numerous hidden layers and learning factors. The model outcome in terms of accuracy is satisfactory. However, the multi-class classification, especially on U2R and Probe, is unsatisfactory. The author in [16] introduced a distributed approach using DBN and an ensemble multi-layer SVM model for large-scale NIDS. The DBN model was utilized to extract the features; the extracted features were then provided as input to the ensemble SVM model. The outcome was performed based on the majority voting technique. The model was validated on four different datasets and offers better accuracy in detecting abnormal behaviors. [PERSON] et al. [17] merged a scalable DNN model to address both HIDS and NIDS abnormal behavior. They utilized the Apache spark cluster tool for experimental purposes. In addition, six different datasets were used to validate the model's performance. The performance of the model is superior to other compared models. However, the author has not specified the class imbalance issue, and thereby it reduces the version of the model. [PERSON] et al. [18] addressed NIDS issues using a Siamese neural network in which they concentrated on eradicating class imbalance issues. The model processes the oversampling and arbitrary under sampling mechanism to mitigate the problems. However, the model performance is not satisfactory in classifying the multi-class intrusions. Later, the same author in [19] improved the Siamese neural network to improve the classification accuracy. They incorporated the DL method to enrich the detection accuracy rate. [PERSON] et al. [20] introduced the LIGO-IDS framework to handle class imbalance issues and to improve classification accuracy. The model incorporates the LSTM technique to classify the multi-class intrusions efficiently.