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ipwx 看了下「 DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning 」摘要，感觉和我需求不大一致。
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Anomaly detection is a critical step towards building a secure and
trustworthy system. The primary purpose of a system log is to
record system states and significant events at various critical points
to help debug system failures and perform root cause analysis. Such
log data is universally available in nearly all computer systems.
Log data is an important and valuable resource for understanding
system status and performance issues; therefore, the various system
logs are naturally excellent source of information for online
monitoring and anomaly detection. We propose DeepLog, a deep
neural network model utilizing Long Short-Term Memory (LSTM),
to model a system log as a natural language sequence. is allows
DeepLog to automatically learn log patterns from normal execution,
and detect anomalies when log patterns deviate from the model
trained from log data under normal execution. In addition, we
demonstrate how to incrementally update the DeepLog model in
an online fashion so that it can adapt to new log patterns over time.
Furthermore, DeepLog constructs workows from the underlying
system log so that once an anomaly is detected, users can diagnose
the detected anomaly and perform root cause analysis effectively.
Extensive experimental evaluations over large log data have shown
that DeepLog has outperformed other existing log-based anomaly
detection methods based on traditional data mining methodologies.
```