Logs are semi-structured text generated by logging statements in software source code. In
recent decades, software logs have become imperative in the reliability assurance …

Automatic log file analysis enables early detection of relevant incidents such as system
failures. In particular, self-learning anomaly detection techniques capture patterns in log …

Software-intensive systems produce logs for troubleshooting purposes. Recently, many
deep learning models have been proposed to automatically detect system anomalies based …

Detecting anomalous events in online computer systems is crucial to protect the systems
from malicious attacks or malfunctions. System logs, which record detailed information of …

Software systems often record important runtime information in system logs for
troubleshooting purposes. There have been many studies that use log data to construct …

With the growth of software systems, logs have become an important data to aid system
maintenance. Log-based anomaly detection is one of the most important methods for such …

Enterprise systems often produce a large volume of logs to record runtime status and events.
Anomaly detection from system logs is crucial for service management and system …

A microservice system in industry is usually a large-scale distributed system consisting of
dozens to thousands of services running in different machines. An anomaly of the system …

Deep learning (DL) techniques are rapidly developed and have been widely adopted in
practice. However, similar to traditional software systems, DL systems also contain bugs …

The detection of anomalies is an essential data mining task for achieving security and
reliability in computer systems. Logs are a common and major data source for anomaly …