Data augmentation with Generative AI for DoW attack detection in serverless architectures

Serverless computing is one of the latest paradigms in cloud computing. It offers a framework for the development of event-driven applications whose functions are executed in a scalable environment provided by the corresponding cloud platform. In this way, resources are obtained on demand, paying only for the time the function is running. This new model has new vulnerabilities and, therefore, new types of cybersecurity attacks. However, there are still not enough transaction datasets for serverless systems with a sufficient amount of data to develop advanced detection methods for this type of threat. Therefore, we present this dataset that has been built with generative AI to advance the development of models that can effectively deal with these threats.