Abstract
In this work, we investigate the current flaws with identifying network-related errors, and examine how K-Means and Long-Short Term Memory Networks solve these problems. We demonstrate that K-Means is able to classify messages, but not necessary provide meaningful clusters. However, Long-Short Term Memory Networks are able to meet our goals of providing an intelligent clustering of messages by grouping messages that are temporally related. Additionally, Long-Short Term Memory Networks can provide the ability to understand and visualize temporal causality, which unlocks the ability to warn about errors before they happen. We show that LSTMs have a 70% accuracy on classifying network errors, and provide some suggestions on future work.
Abstract (translated)
在这项工作中，我们调查当前存在的与网络有关的错误的缺陷，并研究K-Means和长期短期记忆网络如何解决这些问题。我们证明K-Means能够对消息进行分类，但并不需要提供有意义的集群。然而，长短期记忆网络能够实现我们通过对与时间相关的消息进行分组来提供消息的智能群集的目标。此外，长短期记忆网络可以提供理解和可视化时间因果关系的能力，从而释放在发生错误之前发出警告的能力。我们表明，LSTM在分类网络错误方面具有70％的准确性，并为未来的工作提供一些建议。
URL
https://arxiv.org/abs/1806.02000
