## 1 Introduction  
The digitization of almost every aspect of humans' daily lives has led to an increasing number of connected sensors and digital devices according to the Internet of Things (IoT) paradigm. The online presence of such devices renders them vulnerable to cyber-attacks of various kinds. Cyber-attacks exist in various forms and formats such as DDoS, malware, ransomware and botnets. It is indicative that in 2022 there was a 38% increase in cyber-attacks compared to 2021, according to the Check Point Research blog [1]. Botnets are one of the major cyber-attack types since they can affect many devices in parallel, especially in IoT networks. The increasing complexity of current networks, due to the large number of devices interconnected, also increases the number of vulnerabilities exposed to potential attackers. This creates a major problem for cybersecurity experts as new types of attacks or more complex attacks of the existing types are constantly appearing. These ever-expanding capabilities of attackers, along with outdated cybersecurity systems and the lack of data for properly training advanced systems, impose the need for new technologies and tools that can be able to tackle even unforeseen attacks. Most cyber-attacks are addressed after the attack happens, and cybersecurity experts are, often, called to react and minimize its consequences. In this light, the data available for cyber-attacks mainly consist of data from previous attacks. This practically means that almost all cybersecurity systems are trained based on past attacks, and thus, they are vulnerable to new forms of them. However, most organizations do not share attack data, and therefore, there is a lack of availability of such data, which leads to inefficient training of ML models. During the last two decades, network threats, including botnets, have attracted the interest of the academic and research community. [PERSON] et al. [2] worked on a review article about machine learning and botnet detection, where they define a \"bot\" as a software program installed on a compromised host which can perform a series of malicious activities. Usually, bots are installed using various ways such as back doors, external drives and infected websites or files. Botnets are defined as a group of bot-infected hosts which wait for instructions/commands in order to execute malicious code or other activities on the network to which they are attached [3; 4]. Botnets are a major threat to the security and stability of computer networks worldwide. These networks of compromised devices can be used for a range of malicious activities, including distributed denial-of-service (DDoS) attacks, spamming, phishing and data theft [2]. One of the challenges in combating botnets is their ability to evolve and adapt to changes in the security landscape. To address this challenge, this study examines the use of generative adversarial networks (GANs) for generating botnet data samples. The quality of the generated data, in terms of similarity to the original data, is of utmost importance in order to assure that the synthetically generated data are equivalent in terms of quality for training purposes of machine learning models. Thus, the main goal and the added value of the present work can be summarized as follows:  
* Exploring different GAN architectures for data augmentation. * Examining and highlighting the significance of GANs in the generation process of data that can aid ML solutions. * Providing a data quality assessment procedure through graphical data quality Indicators (DQIs), including cumulative sums, absolute log mean and standard deviation (STD) diagrams, as well as correlation matrices along with heatmaps. * Evaluating the results of the DQIs and indicating the feasibility of the proposed solution. The remainder of this paper is structured as follows: In Section 2, related works in the domain of botnet attack generation methods are explored.