A Benchmark for DDoS Attacks Detection in Microservice Architectures

Microservices have become increasingly popular in modern software architectures due to their scalability and flexibility. However, this architectural paradigm introduces unique security challenges, particularly in the detection and mitigation of cyberattacks. This paper presents a collection of datasets designed to benchmark and evaluate attack detection strategies in microservices applications. The datasets include normal and malicious traffic patterns simulating real-world scenarios and attacks, such as classic DDoS, Slow DDoS, Syn Flood, GET Flood. Data was collected from experiments with a popular benchmark microservice system with diverse services interacting via standard protocols and API gateways. Each entry is labeled to distinguish between benign and malicious activities, providing a robust foundation for training and evaluating machine learning models aimed at intrusion detection. In addition to raw data, the dataset includes metadata detailing the configuration of microservices, the nature of simulated attacks, and the temporal sequence of events. This level of detail ensures that researchers and practitioners can reproduce experiments and gain deeper insights into the behavior of attacks in microservice contexts. By offering these datasets, we aim to facilitate the development of advanced detection algorithms and promote more effective security measures in microservice environments.