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Unsupervised Anomaly Detection in Sequences Using Long Short Term Memory Recurrent Neural Networks

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dc.contributor.author alDosari, Majid S
dc.date.accessioned 2016-05-19T19:54:09Z
dc.date.available 2016-05-19T19:54:09Z
dc.date.issued 2016-04-20
dc.identifier.uri https://hdl.handle.net/1920/10250
dc.description.abstract Long Short Term Memory (LSTM) recurrent neural networks (RNNs) are evaluated for their potential to generically detect anomalies in sequences. First, anomaly detection techniques are surveyed at a high level so that their shortcomings are exposed. The shortcomings are mainly their inflexibility in the use of a context ‘window’ size and/or their suboptimal performance in handling sequences. Furthermore, high-performing techniques for sequences are usually associated with their respective knowledge domains. After discussing these shortcomings, RNNs are exposed mathematically as generic sequence modelers that can handle sequences of arbitrary length. From there, results from experiments using RNNs show their ability to detect anomalies in a set of test sequences. The test sequences had different types of anomalies and unique normal behavior. Given the characteristics of the test data, it was concluded that the RNNs were not only able to generically distinguish rare values in the data (out of context) but were also able to generically distinguish abnormal patterns (in context). In addition to the anomaly detection work, a solution for reproducing computational research is described. The solution addresses reproducing compute applications based on Docker container technology as well as automating the infrastructure that runs the applications. By design, the solution allows the researcher to seamlessly transition from local (test) application execution to remote (production) execution because little distinction is made between local and remote execution. Such flexibility and automation allows the researcher to be more confident of results and more productive, especially when dealing with multiple machines.
dc.language.iso en en_US
dc.subject recurrent neural networks en_US
dc.subject unsupervised learning en_US
dc.subject anomaly detection en_US
dc.subject computational reproducibility en_US
dc.subject time series en_US
dc.subject Machine learning en_US
dc.title Unsupervised Anomaly Detection in Sequences Using Long Short Term Memory Recurrent Neural Networks en_US
dc.type Thesis en


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