ANOMALY DETECTION USING A VARIATIONAL AUTOENCODER NEURAL NETWORK WITH A NOVEL OBJECTIVE FUNCTION AND GAUSSIAN MIXTURE MODEL SELECTION TECHNIQUE
| dc.contributor.advisor | Norton, Matthew | |
| dc.contributor.author | Bowman, Brandon | |
| dc.contributor.department | Operations Research (OR) | |
| dc.contributor.secondreader | Alt, Jonathan K. | |
| dc.date.accessioned | 2019-08-08T23:50:27Z | |
| dc.date.available | 2019-08-08T23:50:27Z | |
| dc.date.issued | 2019-06 | |
| dc.description.abstract | Anomalies in data often convey critical information that can be leveraged in a variety of applications. For the military engaged in combat, this can amount to identifying threats early and preserving a lethal edge over an adversary. In other more benign cases it can corrupt data integrity and lead to ineffective application of other data analysis techniques. To tackle the problem of anomaly detection, there are several common methods provided in statistics and machine learning literature, including variational autoencoders (VAEs). Using a VAE, we develop a novel objective function to improve its performance detecting anomalies. Additionally, we introduce a modeling pipeline that works in the fully unsupervised context, where one does not know the true proportion of anomalies present in the data. To construct this pipeline, we fit reconstruction errors using a Gaussian mixture model (GMM) and select the model whose characteristics best match our performance metrics. Using our approach, we observe an increase in anomalies detected against a standard objective function, and we measure an average improvement of 0.4021 in F1 scores. We show our findings using four labeled benchmark data sets and apply our conclusions on an open-source, unlabeled data set taken from USASpending.gov. | en_US |
| dc.description.distributionstatement | Approved for public release; distribution is unlimited. | |
| dc.description.service | Major, United States Marine Corps | en_US |
| dc.description.uri | http://archive.org/details/anomalydetection1094562853 | |
| dc.identifier.thesisid | 32034 | |
| dc.identifier.uri | https://hdl.handle.net/10945/62853 | |
| dc.publisher | Monterey, CA; Naval Postgraduate School | en_US |
| dc.rights | This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States. | en_US |
| dc.subject.author | anomaly detection | en_US |
| dc.subject.author | neural networks | en_US |
| dc.subject.author | variational autoencoder | en_US |
| dc.subject.author | Gaussian mixture model | en_US |
| dc.title | ANOMALY DETECTION USING A VARIATIONAL AUTOENCODER NEURAL NETWORK WITH A NOVEL OBJECTIVE FUNCTION AND GAUSSIAN MIXTURE MODEL SELECTION TECHNIQUE | en_US |
| dc.type | Thesis | en_US |
| dspace.entity.type | Publication | |
| etd.thesisdegree.discipline | Operations Research | en_US |
| etd.thesisdegree.grantor | Naval Postgraduate School | en_US |
| etd.thesisdegree.level | Masters | en_US |
| etd.thesisdegree.name | Master of Science in Operations Research | en_US |
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