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.
@article{arxiv.1806.02000,
title = {Automating Network Error Detection using Long-Short Term Memory Networks},
author = {Moin Nadeem and Vibhor Nigam and Dimosthenis Anagnostopoulos and Patrick Carretas},
journal= {arXiv preprint arXiv:1806.02000},
year = {2018}
}