Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention

Understand

Prompt and accurate detection of system anomalies is essential to ensure the reliability of software systems.

• Unlike manual efforts that exploit all available run-time information, existing approaches usually leverage only a single type of monitoring data (often logs or metrics) or fail to make effective use of the joint information among different types of data.
• Consequently, many false predictions occur.
• To better understand the manifestations of system anomalies, we conduct a systematical study on a large amount of heterogeneous data, i.e., logs and metrics.