| Kurzfassung
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Outlier Detection in today’s application logs is a difficult task because such applications generate massive amounts of unstructured logs, and the formate of such logs differs from one application to another. Since logs are similar to natural languages and state-of-the-art deep learning algorithms have achieved fantastic performance in natural language processing, we utilize state-of-the-art seq2seq frameworks and their attention mechanisms to detect and explain outliers in application logs. We test our framework with several outlier detection benchmarks and achieve comparable performance to state-of-the-art log outlier detection frameworks.
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