Recording runtime status via logs is common for almost computer system, and detecting
anomalies in logs is crucial for timely identifying malfunctions of systems. However …

Concept drift is one of the most frustrating challenges for learning-based security
applications built on the closeworld assumption of identical distribution between training and …

Detecting anomalies in time series is a vital technique in a wide variety of industrial
application in which sensors monitor expensive machinery. The complexity of this task …

For large-scale distributed systems, it is crucial to efficiently diagnose the root causes of
incidents to maintain high system availability. The recent development of microservice …

Anomaly detection in multivariate time series data is of paramount importance for ensuring
the efficient operation of large-scale systems across diverse domains. However, accurately …

With the growing market of cloud databases, careful detection and elimination of slow
queries are of great importance to service stability. Previous studies focus on optimizing the …

With the booming of online service systems, anomaly detection on multivariate time series,
such as a combination of CPU utilization, average response time, and requests per second …

In large-scale online service systems, software changes are inevitable and frequent. Due to
importing new code or configurations, changes are likely to incur incidents and destroy user …

Cloud systems have become increasingly popular in recent years due to their flexibility and
scalability. Each time cloud computing applications and services hosted on the cloud are …

Abstract System logs record the running status of systems, and log anomaly detection can
help locate anomalies timely to reduce error time and ensure normal operation. Logs in text …