Although the autonomous detection of network status and localization of network faults can be a valuable tool for network and service operators, very few works have investigated this subject. As a result in today’s networks, fault detection and localization remains a mostly manual process. In this article, we propose a machine learning (ML) method that can automatically detect the status of a network and localize faults. Our method uses the decision tree, gradient boosting (GB), and extreme GB ML algorithms to detect the network status as normal, congestion, and network fault. In comparison, existing related work can at best classify the network status as faulty or nonfaulty. Experimental results show that our method yields accuracies of up to 99% on a dataset collected through an emulated network.

中文翻译：

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基于机器学习的网络状态检测与故障定位


尽管网络状态的自主检测和网络故障的定位对于网络和服务运营商来说是一个有价值的工具，但很少有工作研究这个主题。因此，在当今的网络中，故障检测和定位仍然主要是手动过程。在本文中，我们提出了一种机器学习（ML）方法，可以自动检测网络状态并定位故障。我们的方法使用决策树、梯度提升 (GB) 和极限 GB ML 算法来检测网络状态：正常、拥塞和网络故障。相比之下，现有的相关工作最多只能将网络状态分为故障或正常。实验结果表明，我们的方法在通过模拟网络收集的数据集上的准确率高达 99%。