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 …

Logs are primary information resource for fault diagnosis and anomaly detection in large-
scale computer systems, but it is hard to classify anomalies from system logs. Recent studies …

In recent years, cloud computing centers have grown rapidly in size. Analyzing system logs
is an important way for the quality of service monitoring. However, systems produce massive …

Log anomaly detection is a key component in the field of artificial intelligence for IT
operations (AIOps). Considering log data of variant domains, retraining the whole network …

The cyber security industry is rapidly adopting threat hunting as a proactive tool for early and
faster detection of suspected malicious actors. In this paper, we propose a machine learning …

Fully supervised log anomaly detection methods suffer the heavy burden of annotating
massive unlabeled log data. Recently, many semi-supervised methods have been proposed …

System logs are a valuable source of information for monitoring and maintaining the security
and stability of computer systems. Techniques based on Deep Learning and Natural …

Due to the complexity of modern IT services, failures can be manifold, occur at any stage,
and are hard to detect. For this reason, anomaly detection applied to monitoring data such …

The log files generated by networked computer systems contain valuable information that
can be used to monitor system security and stability. Recently, techniques based on Deep …

Nowadays large computers extensively output logs to record the runtime status and it has
become crucial to identify any suspicious or malicious activities from the information …