Multi-modality anomaly detection using fused models

A system, comprising:

• a memory device;
• one or more processor devices operatively coupled with the memory device to perform operations including:
• encoding metric data and log data obtained from a monitored entity into metric representations and log representations by utilizing transformer encoders of a cross-joint variational autoencoder (CJVAE);
• fusing the metric representations and the log representations into a joint context representation by utilizing a fusion transformer encoder of the CJVAE;
• decoding the joint context representation by utilizing transformer decoders of the CJVAE to reconstruct the metric representations and the log representations;
• and
• detecting an anomaly for the monitored entity by aggregating detection results from the CJVAE based on the metric representations and the log representations, a metric-specific detection result from a metric detector, and a log-specific detection result from a log detector to resolve determined issues of the monitored entity caused by the anomaly.

A non-transitory computer program product comprising a computer-readable storage medium including a program code, wherein the program code when executed on a computer causes the computer to perform:

• encoding metric data and log data obtained from a monitored entity into metric representations and log representations by utilizing transformer encoders of a cross-joint variational autoencoder (CJVAE);
• fusing the metric representations and the log representations into a joint context representation by utilizing a fusion transformer encoder of the CJVAE;
• decoding the joint context representation by utilizing transformer decoders of the CJVAE to reconstruct the metric representations and the log representations;
• and
• detecting an anomaly for the monitored entity by aggregating detection results from the CJVAE based on the metric representations and the log representations, a metric-specific detection result from a metric detector, and a log-specific detection result from a log detector to resolve determined issues of the monitored entity caused by the anomaly.