## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Data** & **Result** &
\begin{tabular}{c} **Multiple GAN** \\ **Architectures** \\ \end{tabular} & **ML Classifiers** & **GAN Layers** \\ \hline \multirow{4}{*}{[PERSON] et al. [6]} & \multirow{4}{*}{Network traffic} & Data augmentation & \multirow{4}{*}{No} & \multirow{4}{*}{Yes} & \multirow{4}{*}{6G/4D} \\  & & (oversampling) & & \\ \cline{1-1}  & & improved ML classifier & & & \\ \cline{1-1}  & & precision on & & & \\ \cline{1-1}  & & minority class & & & \\ \hline \multirow{2}{*}{[PERSON] [7]} & \multirow{2}{*}{Network traffic} & Non-sequential scalable & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{6} \\  & & network traffic & & & \\ \cline{1-1}  & & generator using GAN & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [8]} & \multirow{4}{*}{Network traffic} & Improved ML classifier & \multirow{4}{*}{No} & \multirow{4}{*}{Yes} & \multirow{2}{*}{6} \\  & & accuracy when trained & & & \\ \cline{1-1}  & & with generated data. & & & \\ \cline{1-1}  & & Indirect evaluation & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [9]} & Network traffic (real & \multirow{2}{*}{Facebook chat)} & Improved malware data & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{N/A} \\  & Facebook chat) & generation mimicking & & & \\ \cline{1-1}  & & real data. & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [10]} & \multirow{2}{*}{Network traffic} & Data augmentation and & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{N/A} \\  & & comparison of generated & & & \\ \cline{1-1}  & & data with real data using & & & \\ \cline{1-1}  & & Euclidean distance & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [12]} & \multirow{2}{*}{IP-Header traces} & Better scalability–fidelity trade-off & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{N/A} \\ \hline \multirow{4}{*}{[PERSON] et al. [13]} & \multirow{4}{*}{Packet exchange} & Distribution of & \multirow{4}{*}{No} & \multirow{4}{*}{No} & \multirow{2}{*}{3G/3D} \\  & & synthetically generated & & & \\ \cline{1-1}  & & data is also very close to other & & & \\ \cline{1-1}  & & random distributions & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [14]} & \multirow{2}{*}{Malware} & Decreased detection rate & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} & \multirow{2}{*}{13G/15D} \\  & & (around 45\%) for the & & & \\ \cline{1-1}  & & generated samples & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of related literature (Yes, when the study covers the corresponding attribute. No, when the study does not cover the corresponding attribute. N/A, when the information for this attribute is Not Available in the study. G = generator, D = discriminator). As can be seen from Table 1, most of the research efforts discussed in this section do not examine more than one GAN implementation. [PERSON] et al. [15], who presented two GAN implementations, differentiate them only by performing a feature selection process on the input dataset. In addition, [PERSON] et al. [22] engaged two different GAN implementations, without diving deep into the implementation details, but rather evaluated the generated data by using the precision, recall and F1-score over six different ML classifiers trained with them. Another point of the relevant literature that was considered in the context of the present study is the number of layers used. As can be observed, the corresponding median value is close to six. Thus, after studying and examining related works, the present study concluded that almost none of them presented a detailed description of the different GAN implementations developed. To this end, herein, the generated data are evaluated using data quality indicators which provide an explanatory and in-depth data profiling of those data, always in comparison to the original ones. Therefore, this study focuses on GANs and synthetically generated data and not on the performance of ML or DL classifiers or detectors.