Deep Learning with Conditional Generative Adversarial Network Based Intrusion Detection System on Balanced Data

Networks have an important role to play in modern life, and cyber security is an active research area. An Intrusion Detection System (IDS) becomes a crucial cyber security method that monitored the state of hardware and software running in the network. IDS can find attacks in available environments. The Machine Learning (ML) method is one among the emerging approaches that have better performance in the situation they have encountered already, and enjoy a wide variety of applications in outlier analysis speech recognition, pattern detection, and so on. With unbalanced data, the predictive model established utilizing ML method might produce an unacceptable classifier that affects the accuracy of predicting intrusion. Conventionally, researcher workers applied oversampling and undersampling to balance data in the dataset for overcoming these problems. Therefore, this article presents a Deep Learning with Conditional Generative Adversarial Network-based Intrusion Detection System (DLCGAN-IDS) technique on Balanced Data. The goal of the DLCGAN-IDS technique lies in the proper balancing of the network samples and identify the intrusions accurately. To accomplish this, the presented DLCGAN-IDS approach primarily normalizes the input data by employing min-max normalization. For imbalanced data handling, the CGAN approach is used for balancing the sample numbers in the dataset. Finally, DL based Long Short-Term Memory (LS TM) method is enforced for detecting and classifying intrusions in the network. The results of the DLCGAN-IDS method execute on the IDS dataset. The comprehensive outcomes pointed out the superior achievement of the DLCGAN-IDS model over other current algorithms.