Content-Level Anomaly Detection for Heterogeneous Logs

A computer-implemented method for automatically analyzing log contents received via a network and detecting content-level anomalies is presented. The computer-implemented method includes building a statistical model based on contents of a set of training logs and detecting, based on the set of train...

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Bibliographic Details
Main Authors Xu, Jianwu, Debnath, Biplob, Arora, Nipun, Jiang, Guofei, Zong, Bo, Zhang, Hui
Format Patent
LanguageEnglish
Published 21.06.2018
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:A computer-implemented method for automatically analyzing log contents received via a network and detecting content-level anomalies is presented. The computer-implemented method includes building a statistical model based on contents of a set of training logs and detecting, based on the set of training logs, content-level anomalies for a set of testing logs. The method further includes maintaining an index and metadata, generating attributes for fields, editing model capability to incorporate user domain knowledge, detecting anomalies using field attributes, and improving anomaly quality by using user feedback.
Bibliography:Application Number: US201715678751