# Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools +
## 3 Related Work  
In this section, an overview of the existing contributions in the field of study is provided, highlighting the main theories, methodologies, and results that have already been published. This allows us to place our work within the context of the existing literature. In [19], authors take a look at how the _Internet of Things_ has changed various parts of life today, like _healthcare_, _transportation_, _home automation_, and _control systems_ in industries. However, more connected devices mean more security risks, especially from botnets, where many ML and _deep learning_ (DL) techniques have been put out for IoT botnet identification in order to counter these threats. Numerous studies have examined the effectiveness of ML and DL techniques in detecting IoT botnet attacks. Particularly, a recent systematic review analyzed the most effective ML and DL techniques, considering benchmark datasets, evaluation metrics, and data preprocessing techniques. This review included studies published between 2018 and 2023, selecting 25 relevant studies out of 1567 initial records. The results indicate that ML and DL techniques outperform traditional _signature-based_ methods in detecting IoT botnet attacks, although the effectiveness varies depending on the dataset, features used, and evaluation metrics adopted. A key problem in making these techniques is the lack of good IoT botnet datasets. Current datasets might not show _real-life_ attacks well, which makes it hard to apply the proposed solutions broadly. Future studies should aim at making more detailed and varied datasets. Another big issue is the weakness of ML and DL models against attacks from adversaries, which can change input data and lead to wrong classifications, as well as more false positives and negatives. These future studies could also aim at making stronger models that can withstand these attacks. Additionally, because ML and DL techniques typically require sensitive data, privacy issues are a major concern. Therefore, future research should concentrate more on developing techniques that preserve privacy when working with encrypted data. The _real-time_ detection of IoT botnet assaults on devices with limited capabilities is made more difficult by the significant processing resources required by many machine learning and deep learning algorithms nowadays. Future work should therefore concentrate on developing _real-time_, lightweight machine learning and deep learning techniques that run on IoT devices with less computing power. The authors of [20] examine current network security research that uses graphs for data representation and analysis, offering new tools for botnets and intrusion detection. As a result, the use of _graph-based_ analysis to enhance network security and the graphical display of network traffic data has been the subject of numerous studies. These graph-based network security approaches include using different types of graphs to display safety information, using advanced techniques for analyzing these charts, and using specific indicators for detecting and tracking threats. Most research works have examined graph models used to display and store network security information, and these studies highlight how graphs can improve the visualization of complex networks. This makes it easier for analysts to detect threats; however, the wide adoption of these techniques can face several challenges. This includes the need to select the most appropriate graph model and algorithm without extensive domain knowledge. Another important challenge is the underutilization of graph databases and _large-scale_ graph processing systems. Despite the potential of these tools, network security applications have yet to take advantage of these tools to their full potential.