  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.
