Assessing Anonymized System Logs Usefulness for Behavioral Analysis in RNN Models

Research output: Contribution to journalConference articleContributedpeer-review


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 clean the monitoring data before analysis. However, anonymized system logs, in general, do not provide adequate usefulness for the majority of behavioral analysis. Content-aware anonymization mechanisms such as PaRS preserve the correlation of system logs even after anonymization. This work evaluates the usefulness of anonymized system logs taken from the Taurus HPC cluster anonymized using PaRS, for behavioral analysis via recurrent neural network models.


Original languageEnglish
Number of pages12
JournalCEUR Workshop Proceedings
Publication statusPublished - 2 Dec 2022


Title2022 International Workshop on Data-Driven Resilience Research
Abbreviated titleD2R2 2022
Duration6 July 2022
LocationNeues Rathaus & online

External IDs



ASJC Scopus subject areas


  • Data usefulness, System log analysis, Time series analysis