Assessing Anonymized System Logs Usefulness for Behavioral Analysis in RNN Models
This addresses privacy concerns in system log analysis for computing systems, but it is incremental as it builds on existing anonymization methods.
The study evaluated the usefulness of anonymized system logs from the Taurus HPC cluster, anonymized using PaRS, for behavioral analysis with recurrent neural network models, finding that such logs generally lack adequate usefulness for most analyses.
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.