The analysis of logs is a vital activity undertaken for cyber investigation,
digital forensics and fault detection to enhance system and cyber resilience.
However, performing log analysis is a complex task. It requires extensive
knowledge of how the logs are generated and the format of the log entries used.
Also, it requires extensive knowledge or expertise in the identifying anomalous
log entries from normal or benign log entries. This is especially complex when
the forms of anomalous entries are constrained by what are the known forms of
internal or external attacks techniques or the varied forms of disruptions that
may exists. New or evasive forms of such disruptions are difficult to define.
The challenge of log analysis is further complicated by the volume of log
entries. Even with the availability of such log data, labelling such log
entries would be a massive undertaking. Hence this research seeks to address
these challenges with its novel Deep Learning model that learns and improves
itself progressively with inputs or corrections provided when available. The
practical application of such model construct facilitates log analysis or
review with abilities to learn or incorporate new patterns to spot anomalies or
ignore false positives.

本研究提出了一种基于深度学习模型的日志分析方法，旨在提高系统和网络安全韧性，能够学习和改进自身，识别异常日志条目并忽略错误报警。