# Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools +
## 2 Case Study: Detection of Bot Nodes Within Unknown Networks in Cloud Environments  
In this section, we provide an innovative case study that demonstrates how our SIR-GN framework can be applied within Cloud-based environments for botnet detection in cutting-edge big data ecosystems. Figure 1 depicts our case study, demonstrating the implementation of the proposed framework within a Cloud-based environment as part of a big data ecosystem, focused on botnet node identification. This approach makes use of well-known big data management and analysis tools. As depicted in Figure 1, the framework can operate on a Cloud-based computing infrastructure (\"at the nodes\"), enabling the big data prediction phase to utilize a collaborative environment where multiple _Graph Learning Layers_ cooperate effectively to enhance the accuracy of botnet node detection. The referenced big data ecosystem adopts a multi-level architecture, comprising the following levels. 1. _Input Layer_: This layer serves as input for the system, as it collects data from both network traffic and node activities in the Cloud. The collected data may consist of various types of information, such as _logs_, _network traffic measurements_, and _different signals_, these information help in discovering the features and characteristics of the targeted botnet. 2. _Graph Learning Layer_: This part of the system is responsible for generating a graph that contains information about node interactions. At this level, graph learning techniques are used to model the relationships between all nodes in the graph. The goal is to discover groups of nodes exhibiting unusual or suspicious activity. Therefore, the detection of these nodes led to the revealing of the key operational behaviors of active botnets. Additionally, the graph learning model is trained using GNNs. The  
Figure 1: Case study: big data ecosystem for detection of bot nodes within unknown networks in cloud-based environment. functionality of the Graph Learning Layer is supported by the integration of the following components: 1. _Original Graph_: This component represents the primary graph, which provides the foundation for the data used to train and test the models. Graph nodes in the network are represented by circles and connections between them by lines. The graph has different labels or classes, denoted by nodes highlighted in red and blue. 2. _Training set and Test set_: The representation of the original graph provides the opportunity to extract both the training set and test set. These are the data used for training the model and for testing its output. The separation step ensures that the model generalization capability is verified by using unseen data from the model. 3. _K-Fold Split_: This step involves the use of the _K-Fold Cross-Validation_ technique to divide the training set into \(K\) subsets, known as _folds_. This technique permits the model to be trained and validated on a variety of datasets, increasing its accuracy and robustness. 4. _Base Model_: Several instances of GNN are used in the base model, each trained on a different subset of data obtained from _K-Fold Split_. Each GNN\({}_{i}\) (where \(i\) varies from 1 to \(S\), with \(S\) as the number of folds) represents a version of the model that learns from the graph features and the relationships between nodes in a specific data split. 5. _Training and Test Result_: Test results for each GNN are generated for its specific fold, which results in a series of evaluations of the model performance. These outcomes can incorporate metrics such as _precision_, _recall_, and other _accuracy_ metrics. 6. _Average of Results_: The results of the different GNNs are combined through an average (indicated with a summation symbol and a final average) to obtain an overall estimate of model performance. By averaging, the variance in the results can be reduced, and the performance measurement becomes more stable. 7. _Secondary Training Set and Secondary Test Set_: The end of the process involves using secondary training and test sets.