  Software systems often record important runtime information in system logs
for troubleshooting purposes. There have been many studies that use log data to
construct machine learning models for detecting system anomalies. Through our
empirical study, we find that existing log-based anomaly detection approaches
are significantly affected by log parsing errors that are introduced by 1) OOV
(out-of-vocabulary) words, and 2) semantic misunderstandings. The log parsing
errors could cause the loss of important information for anomaly detection. To
address the limitations of existing methods, we propose NeuralLog, a novel
log-based anomaly detection approach that does not require log parsing.
NeuralLog extracts the semantic meaning of raw log messages and represents them
as semantic vectors. These representation vectors are then used to detect
anomalies through a Transformer-based classification model, which can capture
the contextual information from log sequences. Our experimental results show
that the proposed approach can effectively understand the semantic meaning of
log messages and achieve accurate anomaly detection results. Overall, NeuralLog
achieves F1-scores greater than 0.95 on four public datasets, outperforming the
existing approaches.
