论文标题
评估RNN模型中行为分析的匿名系统日志有用性
Assessing Anonymized System Logs Usefulness for Behavioral Analysis in RNN Models
论文作者
论文摘要
系统日志是监视用于分析计算系统行为的数据的常见来源。由于现代计算系统的复杂性和大量收集的监视数据，因此需要自动分析机制。提出了许多机器学习和深度学习方法来应对这一挑战。但是，由于系统中存在敏感数据，它们的分析和存储会引起严重的隐私问题。匿名方法可用于在分析之前清洁监视数据。但是，通常，匿名系统日志并不能为大多数行为分析提供足够的实用性。内容感知的匿名机制（例如PARS）即使在匿名后也可以保留系统日志的相关性。这项工作评估了从金牛座HPC群集使用PARS匿名的匿名系统日志的有用性，用于通过复发的神经网络模型进行行为分析。
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.
