# Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU

arXiv: https://arxiv.org/abs/1911.08744 · HTML: https://mlsanity.com/paper/1911.08744/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [Deep Learning for Anomaly Detection in Log Data: A Survey](https://mlsanity.com/paper/2207.03820/) (2022)
- [OneLog: Towards End-to-End Training in Software Log Anomaly Detection](https://mlsanity.com/paper/2104.07324/) (2021)
- [A unified framework for detecting point and collective anomalies in operating system logs via collaborative transformers](https://mlsanity.com/paper/2512.23380/) (2025)

## Abstract (arXiv)

Log messages are now widely used in software systems. They are important for classification as millions of logs are generated each day. Most logs are unstructured which makes classification a challenge. In this paper, Deep Learning (DL) methods called Auto-LSTM, Auto-BLSTM and Auto-GRU are developed for anomaly detection and log classification. These models are used to convert unstructured log data to extracted features which is suitable for classification algorithms. They are evaluated using four data sets, namely BGL, Openstack, Thunderbird and IMDB. The first three are popular log data sets while the fourth is a movie review data set which is used for sentiment classification. The results obtained show that Auto-LSTM, Auto-BLSTM and Auto-GRU perform better than other well-known algorithms.