通过联合学习为网络设备提供保护隐私的 MTS 异常检测

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shenglin Zhang , Ting Xu , Jun Zhu , Yongqian Sun , Pengxiang Jin , Binpeng Shi , Dan Pei
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引用次数: 0

摘要

在 "维护即服务"(MaaS)的背景下,设备供应商必须开发多变量时间序列(MTS)异常检测模型,以便在不损害客户企业数据隐私的情况下准确识别异常情况。在本文中,我们研究了 MTS 数据模式与无监督自动编码器 (AE) 模型参数之间的关系,结果表明它们高度一致。在此基础上,我们提出了一种基于无监督联合学习(FL)的新型框架--OmniFed,它不仅能解决不同设备上非独立同源(non-iid)分布式数据的异质性问题,还能在确保隐私的前提下实现对设备 MTS 异常的高精度检测。具体来说,OmniFed 使用 AE 模型进行初始化,然后通过联合学习在单个设备上训练本地 AE 模型。最后,OmniFed 根据 AE 模型的参数对设备进行聚类,并使用 FL 训练特定聚类的 MTS 异常检测模型。我们在两个真实数据集上的实验表明,OmniFed 的 F1 分数达到 0.921,明显高于最佳基线方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Privacy-preserving MTS anomaly detection for network devices through federated learning
In the context of Maintenance-as-a-Service (MaaS), it is important for device vendors to develop multivariate time series (MTS) anomaly detection models that can accurately identify anomalies without compromising the privacy of customer enterprises' data. In this paper, we investigate the relationship between MTS data patterns and the parameters of unsupervised autoencoder (AE) models and show that they are highly consistent. Building on this insight, we propose a novel unsupervised federated learning (FL)-based framework called OmniFed, which cannot only address the heterogeneity of non-independent identically (non-iid) distributed data on different devices, but also achieve high-precision detection of device MTS anomalies while ensuring privacy. Specifically, OmniFed is initialized with an AE model and then trains local AE models on individual devices via federated learning. Finally, OmniFed clusters devices based on the parameters of the AE models and trains a cluster-specific MTS anomaly detection model using FL. Our experiments on two real-world datasets demonstrate that OmniFed achieves an F1-Score of 0.921, significantly higher than the best baseline method.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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