  The analysis of logs is a vital activity undertaken for fault or cyber
incident detection, investigation and technical forensics analysis for system
and cyber resilience. The potential application of AI algorithms for Log
analysis could augment such complex and laborious tasks. However, such solution
has its constraints the heterogeneity of log sources and limited to no labels
for training a classifier. When such labels become available, the need for the
classifier to be updated. This practice-based research seeks to address these
challenges with the use of Transformer construct to train a new model with only
normal log entries. Log augmentation through multiple forms of perturbation is
applied as a form of self-supervised training for feature learning. The model
is further finetuned using a form of reinforcement learning with a limited set
of label samples to mimic real-world situation with the availability of labels.
The experimental results of our model construct show promise with comparative
evaluation measurements paving the way for future practical applications.
