## 1 Introduction  
In recent years, the evolution of information technology and security protocols has increased exponentially in network traffic data [1]. Most computer applications are allied to cyberspace for efficient services of various applications such as browsing, social media, e-mails, etc. In addition, different security modules are invoked into network applications to tackle network intrusions. Network intrusions are unsolicited traffic behaviors that are prone to malicious attacks and are harmful to host networks. The hostile invasions are prone to various attacks, including denial-of-service (DoS) attacks, stealing user information by ID theft attacks, phishing attacks, etc. Therefore, it leads to the growth of security problems in cloud storage and leaks the confidentiality of users' data in a communal environment. The intruders execute these attacks with malicious nodes or malware to compromise the host system. Hence, the network security researchers introduced an intrusion detection system to handle anomalous networks [2]. IDS is recognized as one of the most powerful and promising techniques. It aids in perceiving threats and malicious actions by monitoring computer traffic information, and signals are raised when the threats are noticed. Generally, observing malicious activities is characterized into two processes: signature-based discovery and anomaly-based discovery. The signature-based detection method works like an antivirus application that compares the current task with historical task features. In contrast, the anomaly-based detection method works based on the comparison with regular traffic to process the decision. In the NSL-KDD dataset, the network attacks are characterized into four major divisions,namely remote-to-local (R2L), denial-of-service (DoS), probe attack, and user-to-root (U2R) attacks [3]. Many network security researchers have recently incorporated learning models into intrusion detection systems to determine accurate network attacks. Learning models are utilized more because of their efficacy in processing large-dimension data, evolutionary-based learning capability, and automatic feature extraction. Most recent works used traditional machine learning models to handle the intrusion detection, namely support vector machine [4], XGBoost [5], naive Bayes [6], KNN [7], and random forest [8]. In addition, deep learning models have been utilized in several recent works, namely recurrent neural networks [9], multilayer perceptron [10], and convolutional neural networks [11]. They have proven their efficacy in detecting attacks with improved accuracy. Nevertheless, the existing techniques have made thoughtful improvements, but the class-imbalanced information remains a challenging issue that hampers the performance of most IDS. The class imbalance arises when the standard trials are significantly higher than the number of intruder trials. Therefore, traditional activities dominate in real networks, thus lead to the misclassification of intrusion detection [12]. A quantitative indicator to determine the severity of class imbalance is the disparity ratio between the dominant and marginal classes. For illustration, the practical dataset of network incursion has nearly 22 lakhs of standard trials and only 5 lakhs of intrusion trials. Thus, the imbalance ratio is computed as 4.4:1. An observation is noticed that most of the samples belong to majority classes while abandoning the minority samples. The trials of minority classes are minimal and insufficient for those techniques. However, the methods are inadequate to learn from the minimal minority samples, and the outcomes are towards the majority. The misdetection of minority samples (intrusion) is much more critical than detecting the standard trials as an intrusion. In this work, we introduced a novel learning technique, modified conditional generative adversarial network (MCGAN), to handle the class disparity issue, which generates adequate trials for minority classes.