论文标题
使用1-D数据系列上快速，准确的异常定位和分类的主动网络维护
Proactive Network Maintenance using Fast, Accurate Anomaly Localization and Classification on 1-D Data Series
论文作者
论文摘要
主动网络维护（PNM）是使用网络数据识别和定位网络故障的概念，许多或全部可能会恶化以成为服务故障。网络故障与服务故障之间的分离使网络中的问题早期发现，以允许PNM发生。因此，PNM是预后和健康管理（PHM）的一种形式。 在一维数据序列上本地化和分类异常的问题已经在研究中多年。我们引入了一种新算法，该算法利用深度卷积神经网络有效，准确地检测数据系列的异常和事件，并且在我们的评估中达到了97.82％的平均平均精度（MAP）。
Proactive network maintenance (PNM) is the concept of using data from a network to identify and locate network faults, many or all of which could worsen to become service failures. The separation between the network fault and the service failure affords early detection of problems in the network to allow PNM to take place. Consequently, PNM is a form of prognostics and health management (PHM). The problem of localizing and classifying anomalies on 1-dimensional data series has been under research for years. We introduce a new algorithm that leverages Deep Convolutional Neural Networks to efficiently and accurately detect anomalies and events on data series, and it reaches 97.82% mean average precision (mAP) in our evaluation.
