## 2 Related Works  
The continuous increase in botnet attacks and the damage effects for individuals as well as organizations has led to the increasing need for new solutions or the enhancement of existing ones. This need also attracted academic and research interest; thus, several studies focusing in greater depth on botnet attack generation methods are available. Considering the focus of this study, which is to evaluate the generated botnet attack samples, the remainder of this section is focused on relevant works that were published during the last decade and are mainly focused on cyber-attack data generation procedures using GANs. Firstly, an interesting approach regarding network traffic generation data was presented by [PERSON] and [PERSON] [5]. Their study indicated that solutions engaging GANs can overcome the limitations of other approaches such as Poisson models. More specifically, the authors studied the findings of six different GANs: ITCGAN (Imbalanced Traffic Classification GAN) [6], PAC-GAN (Packet GAN) [7], PcapGAN [8], Facebook Chat Network Traffic GAN [9], Flow-Based Network Traffic Generation GAN [10] and ZipNet GAN [11]. All of these approaches feature certain strengths; e.g., ITCGAN paved a new way for addressing the data imbalance issue, whilst the PAC-GAN achieved the generation of network traffic flows at the packet byte level [5]. Another interesting approach presented by [PERSON] et al. [12] was NetShare. NetShare was an end-to-end framework that used GAN architecture in order to produce IP-Header trace generation data. In addition, NetShare achieved 46% better fidelity compared to other baselines. Moreover, it achieved a better scalability-fidelity trade-off compared to other existing solutions [12]. In a similar direction, [PERSON] et al. [13] proposed the Synthetic Packet Traffic Generative Adversarial Network (SPATGAN) approach. The SPATGAN framework consisted of a server agent and a client agent to simulate/mimic the exchange of packets over a network. More specifically, this architecture engaged other two GANs, namely the Timing Synthesis GAN (TSynGAN) and the Packet Synthesis GAN (PSyGAN) for each agent, respectively. The results indicated that the distribution of the generated samples was very close to the real ones. Furthermore, the Frechet Traffic Distance (FTD) score indicated that the distribution of synthetically generated data was, also, very close to other random distributions [13]. [PERSON] et al. [14] presented the MalFox solution which aims to prove the inefficiency of already existing black box detectors. The MalFox is based on a convolutional GAN, and its aim ultimately is to bypass malware detectors. Their solution adopts a confrontational approach to produce perturbation paths, with each one formed by up to three methods (namely Obfusmal, Stealmal and Hollowmal) to generate adversarial malware examples. The acquired performance results were quite encouraging with an accuracy of around 99%, while the detection rate of the generated samples led to a low percentage of almost 45% [14]. The significance of GANs for data augmentation, especially in the cybersecurity domain, was also indicated by the Conditional Tabular GAN (CTGAN) model proposed by [PERSON] et al. [15]. In their work, the authors tested different CTGAN versions with alternate parameters to identify the most efficient one. The results of their experiments indicated that the CTGAN could preserve the structure of both continuous and discrete data. This led to a solution for ML classifiers or detectors by alleviating the imbalance in datasets and, also, training these ML algorithms for unknown threats as the data generated by the GANs are new and unseen [15]. [PERSON] et al. [16] also performed a study about imbalanced data related to bot identification. In their study, they aimed to address the problem of imbalanced data for ML classifiers by engaging a GAN with a gated recurrent unit (GRU).