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Discovering Sequences in Systems Logs by Neural Networks

Computer and network system logs consist of an enormous number of various events. By discovering sequences of events related to a system failure, we can identify its causes. It is usually a difficult task, as many various factors can contribute to the failure. In this paper, we use a deep neural network to discover unknown sequences in system logs as an alternative to sequential pattern mining algorithms. We train the neural network to recognize repetitive sequences and test it on unseen real sequences of events from system logs. The logs are one-hot, event-level encoded. The network achieves very high accuracy and is faster than traditional algorithms for sequence discovery.