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.
%0 Journal Article
%1 Vargis2022-ye
%A Vargis, Tom Richard
%A Ghiasvand, Siavash
%D 2022
%I arXiv
%K
%T Assessing anonymized system logs usefulness for behavioral analysis in RNN models
%X 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.
@article{Vargis2022-ye,
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 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.},
added-at = {2024-09-10T11:56:37.000+0200},
author = {Vargis, Tom Richard and Ghiasvand, Siavash},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/293ea80100feeacd3b95b30e52fb35c78/scadsfct},
interhash = {d2ed8ddf915e587ee3ccb8c6ce25bc3e},
intrahash = {93ea80100feeacd3b95b30e52fb35c78},
keywords = {},
publisher = {arXiv},
timestamp = {2024-09-10T15:15:57.000+0200},
title = {Assessing anonymized system logs usefulness for behavioral analysis in {RNN} models},
year = 2022
}
