This paper presents HURRA, a system that aims to reduce the time spent by human operators in the process of network troubleshooting. To do so, it comprises two modules that are plugged after any anomaly detection algorithm: (i) a first attention mechanism, that ranks the present features in terms of their relation with the anomaly and (ii) a second module able to incorporates previous expert knowledge seamlessly, without any need of human interaction nor decisions. We show the efficacy of these simple processes on a collection of real router datasets obtained from tens of ISPs which exhibit a rich variety of anomalies and very heterogeneous set of KPIs, on which we gather manually annotated ground truth by the operator solving the troubleshooting ticket. Our experimental evaluation shows that (i) the proposed system is effective in achieving high levels of agreement with the expert, that (ii) even a simple statistical approach is able to extracting useful information from expert knowledge gained in past cases to further improve performance and finally that (iii) the main difficulty in live deployment concerns the automated selection of the anomaly detection algorithm and the tuning of its hyper-parameters.

中文翻译：

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万岁！人类可读的路由器异常检测

本文介绍了 HURRA，这是一个旨在减少人工操作员在网络故障排除过程中花费的时间的系统。为此，它包括在任何异常检测算法之后插入的两个模块：（i）第一个注意机制，根据它们与异常的关系对当前特征进行排名，以及（ii）第二个模块能够结合以前的专家无缝地获取知识，无需任何人为交互或决策。我们展示了这些简单过程对从数十个 ISP 获得的真实路由器数据集的有效性，这些数据集表现出丰富多样的异常和非常异构的 KPI 集，我们通过解决故障排除单的操作员在这些数据集上收集手动注释的基本事实。