The shift towards cloud-native applications has been accelerating in recent years, constituting a paradigm shift that is heavily distributed by nature, taking advantage of features such as scalability, flexibility, and high availability. However, this evolution also introduces various security challenges. From a networking perspective, the large number of interconnected components and their intricate communication patterns make detecting and mitigating traffic anomalies a complex task. Beyond identifying network traffic anomalies, it is also important to identify their source, as well as to be able to trust the outputs of the selected AI/ML techniques and models, which should also be resilient to attacks coming from other AI/ML models (which is common in adversarial scenarios). Nevertheless, to be able to develop such models, suitable cloud-native datasets are required, ideally accompanied by a known and comparable feature vector, which is often insufficient or nonexistent at all—to tackle this issue, this paper presents the ONE-MS-1 dataset.