Multi-feature log anomaly detection method and system based on log full semantics

• Main Authors: Niu, Weina, Li, Zimu, Zhang, Xiaosong
• Format: Patent
• Language: English
• Published: 22.12.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS

A multi-feature log anomaly detection method includes steps of: preliminarily processing a log data set to obtain a log entry word group corresponding to all semantics of a log sequence in the log data set, and using the log entry word group as a semantic feature of the log sequence; extracting a type feature, a time feature and a quantity feature of the log sequence, and encoding the semantic feature, the type feature, the time feature and the quantity feature into a log feature vector set of the log sequence; training a BiGRU neural network model with all log feature vector sets to obtain a trained BiGRU neural network mode; and inputting the log data set to be detected into the trained BiGRU neural network model for prediction, and determining whether the log sequence is a normal or abnormal log sequence according to a prediction result.