## 2 Literature Review
### ML-Based IDS Using GAN Methods  
Although the identified ML algorithm pipelines obtained a very high level of accuracy, the IDS still could not be properly trained using insufficient and imbalanced datasets. The size of the abnormal/attack data is too small compared to the normal data in most published datasets, creating a data imbalance problem during training. Furthermore, the distribution of the data space cannot be apparent to the IDS due to some missing data,even if there is a sufficient amount of data. Generative adversarial networks (GANs) are used to generate new synthetic similar samples in order to solve these problems. [PERSON] et al. [28] implemented a GAN-IDS framework model for the NSL-KDD'99 dataset. They reported that the GAN-IDS framework predicted better accuracy than an independent IDS. However, further investigation is required to elaborate on issues and limitations such as centralization, computational expenses, and the time-consuming nature of the GAN-IDS framework. [PERSON] et al. [29] considered the imbalance and high dimensionality of datasets in intrusion detection and suggested an oversampling intrusion detection technique based on GAN and feature selection. They oversampled the rare classes of attack samples using GAN to generate a rebalanced low-dimensional dataset for ML training and improved the efficiency of intrusion detection. They implemented this approach on the NSL-KDD, UNSW-NB15, and CICIDS-2017 datasets and reported effective improvements in the detection performance of ML models. Additionally, [PERSON] et al. [30] suggested a GAN framework called IDSGAN for adversarial malicious attack generation against an IDS. An IDSGAN generates malicious feature records to attack the network by deceiving and evading detection. It can attack real-time IDS models powered by several ML algorithms and maintain malicious traffic functionalities. They implemented the IDSGAN in the NSL-KDD dataset, reported its effectiveness in generating adversarial malicious traffic records of different classes of attacks, and lowered the detection rates of various IDS models to approximately 0%. [PERSON] and [PERSON] [31] proposed a synthetic IDS dataset generation model using a Wasserstein conditional generative adversarial network with an XGBoost classifier, and they used the feature reduction method based on a deep autoencoder. The applied datasets were NSL-KDD, UNSW-NB15, and BoT-IoT; the F1 score for each dataset was 0.96, 0.81, and 0.99, respectively. However, the quality of the WCGAN model on the ML classifiers was assessed using a mix of the actual and generated samples instead of using only the generated samples for training. Moreover, the features were not unified among all datasets, and the high detection performance was limited to the NSL-KDD dataset. Additionally, although the IoT environment was one of the main focuses of the study, the reduction in training and testing times has not been considered. [PERSON] et al. [32] presented deep reinforcement learning using the NSL-KDD dataset generated by conditional and unconditional CTGAN and CopulaGAN. They found that CTGAN had the highest accuracy (0.85) among the other studied GANs. However, feature selection and time reduction were not considered in the methodology. In addition, the generated dataset was not assessed; the generated dataset was used in training and the real dataset was used during testing, which may provide a deeper understanding of whether the generated dataset can replace the real dataset. The abovementioned studies identified several ML algorithm pipelines using various datasets. However, these datasets neither focus on IoT attacks nor cover all IoT attacks, such as MQTT attacks. Thus, they may not be appropriate for safeguarding IoT networks. To address this problem, [PERSON] et al. [33] created a new dataset that focused on the MQTT protocol and was widely adopted in IoT networks. They demonstrated that MQTTset can be used to train ML algorithms to implement IDSs in order to protect IoT networks.