Abstract: An anomaly detection system, an anomaly detection method, an electronic device and a storage medium are provided, which relates to the field of artificial intelligence. The anomaly detection system includes a plurality of markers. The plurality of markers are different from each other and correspond to different anomaly detection operations. A log line of a target system may be detected from different dimensions by using the plurality of markers, and marker tokens generated by different markers are obtained. In addition, the anomaly detection system further includes a model marker. The model marker may combine the marker tokens respectively generated by each marker into a token sequence and perform anomaly detection on the token sequence by using a pre-trained neural network model to obtain an overall anomaly score of the target system within a preset time period.

Abstract: A method and an apparatus for detecting an anomaly based on time information, an electronic device and a medium. The method includes: dividing a preset detection period into multiple preset time intervals; performing different anomaly detection operations on log lines generated by a target system during each of the multiple preset time intervals to obtain anomaly scores of different detection types corresponding to the preset time interval; adding preset detection type tokens to discrete anomaly scores to obtain marker token data, and adding a preset timestamp token to a timestamp corresponding to the preset time interval to obtain timestamp token data; and combining the timestamp token data and the marker token data to form a token sequence, performing anomaly detection on the token sequence by using a pre-trained detection model to determine whether the target system is anomalous.

Abstract: An anomalous cause detection method, apparatus, electronic device and storage medium are provided, relate to the field of artificial intelligence. Multiple types of anomaly detection operations are performed on logs generated by a target system to obtain anomaly scores for respective detection types. Anomaly score time-series curves corresponding to the detection types are generated. Correlation analysis is performed on the anomaly score time-series curves. Correlated anomaly score time-series curves are fused into a comprehensive anomaly score time-series curve. A detection type corresponding to the comprehensive anomaly score time-series curve is determined. Anomaly detection is performed on the comprehensive anomaly score time-series curve, and an anomaly time instant is determined.