## 2 Related Works
This enabled them to produce synthetic data that were very similar to the real data and, thus, to balance the classes of benign users and bots. The results indicated that their solution outperformed ML solutions that were only trained with the original Twitter dataset, and the average accuracy of all the methods evaluated with the GAN-generated dataset achieved a precision of around 91% [16]. [PERSON] et al. [17], also, focused on a solution that enhances botnet detection. Their study included a GAN which could create almost realistic botnet attack samples in order to train better ML classifiers. The Bot-GAN proposed constantly provided \"fake\" data to the discriminator which, through a softmax function, classified the samples. In this way, the accuracy and precision were improved compared to pretrained models with the original imbalanced dataset [17]. Following the same path to lift the limitations of an imbalanced dataset, [PERSON] et al. [18] proposed the GAN-Efficient Lifelong Learning Algorithm (ELLA) solution. Their approach indicated that a dataset expanded via a GAN architecture enhanced both the results of traditional ML solutions for identifying botnets as well as the lifelong approach of the ELLA algorithm [18]. Furthermore, [PERSON] et al. [19] proposed the GANIBOT solution which is a semi-supervised GAN model for IoT botnet detection. Their solution engaged a GAN model which performed better for binary classification of the N-BaIoT dataset [20] as well as a supervised learning classifier for multiclass classification. Both were integrated into a common framework to provide a semi-supervised solution for botnet detection; thus, their solution is referred to as a semi-supervised GAN (SGAN). The results of their study indicated that the SGAN outperformed other ML methods such as artificial neural networks (ANNs) and convolutional neural network (CNNs), and after fine-tuning of the model parameters, it achieved even better results in terms of both accuracy and computational efficiency [19]. In order to tackle an imbalanced dataset and ingest more samples of the minority class, [PERSON] and [PERSON] [21] proposed a GAN with many convolutional layers. Their solution proved quite efficient as the fused bidirectional long short-term memory attention model (SFBAM) achieved an F1-score of around 96% on the IoT-23 dataset when it was augmented with GAN-generated samples [21]. Following a similar path, [PERSON] et al. [22], proposed the botshot framework. Botshot is based on two GANs, one vanilla and one conditional, in order to augment already imbalanced IoT datasets so as to enhance the performance of ML classifiers [22]. In Table 1, an overview of the related works is presented, along with some key observations. The Data column indicates the type of data used for each study, the Result column briefly describes the main findings/contribution of the corresponding study, the Multiple GAN Architectures column indicates if this study contains more than one GAN implementation, the ML Classifiers column indicates if a study uses ML classifier performance to evaluate GANs, and finally, the GAN Layers column gives information on the number of layers used for generator/discriminator implementations.