Classification of Home Network Problems with Transformers
Jeremias Dötterl Zahra Hemmati Fard
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.