## 2 Related Work  
The use of Machine-Learning (ML) and/or Deep-Learning (DL) techniques is a recurring theme in several studies, especially in IoT and cloud environments [8; 9]. One approach, introduced by [10], proposes the use of models such as Random Forest and Gradient Boosting for DDoS attack detection, which demonstrated high accuracy when applied to network traffic datasets. These models benefit from feature selection techniques, such as Mutual Information and Random Forest Importance Index (RFFI), to improve accuracy in threat detection by selecting the most relevant features for analysis [11]. In the IoT context, the study by [12], which proposes a botnet detection model using a CVAE, using advanced feature selection and hyperparameter tuning to improve algorithm performance, achieving a high detection accuracy of 99.50%, stands out. As for cloud environments, the challenge in detecting DDoS attacks is addressed through various machine-learning techniques, as proposed by [13], where SVMs are used in conjunction with Random Forest, used to analyze network traffic and detect anomalous patterns. Additionally, Deep-Learning (DL) models such as CNN and LSTM are used in conjunction with ensemble methods such as Random Forest, standing out for their high accuracy in detecting DDoS attack scenarios, especially when used in conjunction with mechanisms such as Bi-LSTM, as proposed by [5]. The integration of these models in IoT/Cloud environments is a major challenge, due to the dynamic and sophisticated nature of DDoS attacks, and the use of AI-based methods is increasingly relevant to improve the detection and mitigation of these attacks, given their ability to learn and adapt to various network behaviors associated with anomalous activities associated with DDoS attacks, as pointed out by [13]. Advances in the development of new technologies associated with IoT have made it possible to increase the automation of processes and improve connectivity in various sectors, from industrial applications to use in domestic devices (home automation). However, the growth in the number of devices connected to the network has expanded the area of attack, exposing them to increasingly sophisticated threats. Among the most relevant threats are Distributed Denial-of-Service (DDoS) attacks, which have intensified in both frequency and sophistication, and which seek to saturate the resources of a system or network through multiple simultaneous requests, so that legitimate users cannot access information or services, thereby affecting their availability. According to [PERSON] et al. [14], the distributed nature of IoT devices, in conjunction with their constant connectivity in cloud-based networks, creates an ideal environment for these attacks. The increasing vulnerability of IoT devices to Distributed Denial-of-Service (DDoS) attacks has driven the development of advanced detection models. Particularly in industrial environments, machine-learning- and deep-learning-based approaches have been shown to offer superior adaptation and detection capabilities against emerging threats [15]. IoT/Cloud environments are an attractive target for attackers, who seek to compromise the network and turn each device into an attack node (known as a _bot_). The large number of devices connected to this type of architectures allows the creation of _botnets_, networks of compromised devices, which can be remotely controlled and enable large-scale DDoS attacks (as can be seen in Figure 1), representing a significant threat to the stability of both industrial and domestic services. IoT devices frequently present security problems, especially linked to timely firmware updates, which consequently makes them perfect targets through which coordinated attacks can be executed, and as a direct result of this vulnerability, the critical challenge involved in the process of identifying and analyzing anomalous network traffic, and specifically within the aforementioned environments, is identified.