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Anomaly Detection using Distributed Log Data: A Lightweight Federated Learning Approach

机译：使用分布式日志数据的异常检测：一种轻量级联合学习方法

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摘要

Large-scale software systems are generally deployed on distributed machines. Logs are usually collected from those machines for comprehensive and accurate system fault analysis. However, there are potential challenges during log transmission from distributed machines to third-party data analytics services. First, uploading massive raw logs causes tremendous bandwidth consumption. Moreover, user privacy contained in logs is easy to get leaked during transmission. To address these issues, we introduce federated learning for anomaly detection using distributed log data. However, gradient updates of model parameters transmitted between the server (third-party data analytics services) and participants (distributed machines) in federated learning have been proved of possible recovery by attackers, so encryption of gradient updates is necessary for enhanced privacy protection. Considering that encryption time is proportional to the number of parameters, we propose a lightweight federated learning method for anomaly detection, named FLOGCNN, using distributed log data. The sever in FLOGCNN aggregates gradient updates according to the sample size of participants to generate an integrated model. For local training, participants apply an anomaly detection model based on one-dimensional convolution with much fewer parameters. Extensive experiments are conducted for FLOGCNN using open log datasets. Results demonstrate that FLOGCNN outperforms baseline methods on anomaly detection and reduces 97.08% parameters in comparison with one baseline method. Furthermore, we perform exploratory experiments on lightweight models and results manifest that logs with simple semantic information are suitable for lightweight anomaly detection models.
机译：大规模软件系统通常部署在分布式机器上。通常从这些机器上收集日志，以进行全面、准确的系统故障分析。然而，在从分布式计算机到第三方数据分析服务的日志传输过程中，存在潜在的挑战。首先，上传大量原始日志会造成巨大的带宽消耗。此外，日志中包含的用户隐私在传输过程中很容易被泄露。为了解决这些问题，我们引入了使用分布式日志数据进行异常检测的联邦学习。然而，在联邦学习中，服务器（第三方数据分析服务）和参与者（分布式机器）之间传输的模型参数的梯度更新已被证明可能被攻击者恢复，因此梯度更新的加密对于增强隐私保护是必要的。考虑到加密时间与参数数量成正比，我们提出了一种基于分布式日志数据的轻量级联邦学习异常检测方法FLOGCNN。FLOGCNN中的服务器根据参与者的样本大小聚合梯度更新，以生成集成模型。对于局部训练，参与者采用基于一维卷积的异常检测模型，参数少得多。使用开放日志数据集对FLOGCNN进行了大量实验。结果表明，FLOGCNN在异常检测方面优于基线方法，与单一基线方法相比，参数减少了97.08%。此外，我们对轻量级模型进行了探索性实验，结果表明，具有简单语义信息的日志适合轻量级异常检测模型。

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