# Assessing Anonymized System Logs Usefulness for Behavioral Analysis in RNN Models

arXiv: https://arxiv.org/abs/2212.01101 · HTML: https://mlsanity.com/paper/2212.01101/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection](https://mlsanity.com/paper/2107.05908/) (2021)
- [DANTE: Predicting Insider Threat using LSTM on system logs](https://mlsanity.com/paper/2102.05600/) (2021)
- [Anomaly Detection in High Performance Computers: A Vicinity Perspective](https://mlsanity.com/paper/1906.04550/) (2019)
- [Anonymization of System Logs for Privacy and Storage Benefits](https://mlsanity.com/paper/1706.04337/) (2017)

## Abstract (arXiv)

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.