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is paper presents DeepLog, a general-purpose framework for online log anomaly detection and diagnosis using a deep neural network based approach. DeepLog learns and encodes entire log message including timestamp, log key, and parameter values. It performs anomaly detection at per log entry level, rather than at per session level as many previous methods are limited to. DeepLog can separate out dierent tasks from a log le and construct a work- ow model for each task using both deep learning (LSTM) and classic mining (density clustering) approaches. is enables eective anomaly diagnosis. By incorporating user feedback, DeepLog supports online update/training to its LSTM models, hence is able to incorporate and adapt to new execution paerns. Extensive evaluation on large system logs have clearly demonstrated the superior eectiveness of DeepLog compared with previous methods. Future work include but are not limited to incorporating other types of RNNs (recurrent neural networks) into DeepLog to test their eciency, and integrating log data from dierent applications and systems to perform more comprehensive system diagnosis (e.g., failure of a MySQL database may be caused by a disk failure as reected in a separate system log).
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