The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/20463687

Clinton Cao; Agathe Blaise (2022): AssureMOSS Kubernetes Run-time Monitoring Dataset. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/20463687.v1

Dataset

• Other Information and Computing Sciences

Anomaly detection (Computer security), Kubernetes cluster, NetFlow, run-time monitoring

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Clinton Cao , Agathe Blaise

AssureMOSS Kubernetes Run-time Monitoring Dataset

This dataset contains NetFlow data that is collected from a Kubernetes cluster. The cluster is used to monitor the microservice applications that are running on the cluster. The goal is to use the (NetFlow) logs to learn a state machine model that models the normal network behaviour within the cluster. The state machine model is then used to monitor and detect potential anomalies that might occur during the runtime of the cluster. This dataset contains both benign data (produced by real-life users) and malicious data (produced by launching several attacks against the clusters). The label of each flow is included in the dataset.

• 2022-08-10 first online, published, posted

4TU.ResearchData

CSV

• https://doi.org/10.1145/3538969.3543810
• https://arxiv.org/abs/2207.12087
• https://arxiv.org/abs/2207.03890

• Assurance and certification in secure Multi-party Open Software and Services. (grant code 952647) [more info...] European Commission

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems
Thales SIX GTS France SAS