We propose a classifier that can identify ten common home network problems based on the raw textual output of networking tools such as ping, dig, and ip. Our deep learning model uses an encoder-only transformer architecture with a particular pre-tokenizer that we propose for splitting the tool output into token sequences. The use of transformers distinguishes our approach from related work on network problem classification, which still primarily relies on non-deep-learning methods. Our model achieves high accuracy in our experiments, demonstrating the high potential of transformer-based problem classification for the home network.

翻译：我们提出了一种分类器，能够根据ping、dig和ip等网络工具输出的原始文本，识别十种常见家庭网络问题。该深度学习模型采用仅编码器的Transformer架构，并配套使用一种专为将工具输出拆分为令牌序列而设计的预分词器。与仍主要依赖非深度学习方法的网络问题分类相关研究不同，Transformer的使用使我们的方法脱颖而出。实验表明，该模型实现了高准确率，充分展示了基于Transformer的问题分类在家庭网络中的巨大潜力。