LogIntelligence/NeuralLog
Log-based Anomaly Detection Without Log Parsing (ASE 2021, Research Track)
This project helps operations engineers and system administrators automatically find unusual behavior in their systems. It takes raw system logs as input and identifies potential anomalies, helping you quickly spot problems that need attention. The output is a classification of log sequences, indicating whether they are normal or anomalous.
141 stars. No commits in the last 6 months.
Use this if you need to detect system anomalies from raw log data and are concerned that traditional log parsing methods might miss important clues due to errors or misunderstandings.
Not ideal if you need to perform detailed forensic analysis that requires precise parsing and understanding of every log field.
Stars
141
Forks
42
Language
Python
License
MIT
Category
Last pushed
Jun 20, 2023
Commits (30d)
0
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