Assessing Anonymized System Logs Usefulness for Behavioral Analysis in RNN Models Tom Richard Vargis1,β, Siavash Ghiasvand1,2 1Technische UniversitΓ€t Dresden, Germany 2Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Germany Abstract System logs are a common source of monitoring data for analyzing computing systems behavior. Due to the complexity of modern computing systems and the large size of collected monitoring data, automated analysis mechanisms are required. Numerous machine learning and deep learning methods are proposed to address this challenge. However, due to the existence of sensitive data in system logs their analysis and storage raise serious privacy concerns. Anonymization methods could be used to cleanse the monitoring data before analysis. However, anonymized system logs in general do not provide an adequate usefulness for majority of behavioral analysis. Content-aware anonymization mechanisms such as ππΌπ π preserve the correlation of system logs even after anonymization. This work evaluates the usefulness of anonymized system logs of Taurus HPC cluster anonymized using ππΌπ π, for behavioural analysis via recurrent neural network models. To facilitate the reproducibility and further development of this work, the implemented prototype and monitoring data are publicly available [12].
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:92793 |
| Date | 06 August 2024 |
| Creators | Vagis, Tom Richard, Ghiasvand, Siavash |
| Publisher | RWTH Aachen |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 1613-0073 |
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