Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/8892
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gjorgjiev, L. | en_US |
dc.contributor.author | Gievska, Sonja | en_US |
dc.date.accessioned | 2020-09-04T17:34:06Z | - |
dc.date.available | 2020-09-04T17:34:06Z | - |
dc.date.issued | 2020-09 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12188/8892 | - |
dc.description.abstract | Two themes have dominated the research on anomaly de- tection in time series data, one related to explorations of deep architec- tures for the task, and the other, equally important, the creation of large benchmark datasets. In line with the current trends, we have proposed several deep learning architectures based on Variational Autoencoders that have been evaluated for detecting cyber-attacks on water distribution system on the BATADAL challenge task and dataset. The second research aim of this study was to examine the impact of using Mahalanobis distance as a reconstruction error on the performance of the proposed models. | en_US |
dc.publisher | Springer | en_US |
dc.subject | Time series analysis · Anomaly detection · Variational au- toencoder · Mahalanobis distance. | en_US |
dc.title | Time series anomaly detection with Variational Autoencoder using Mahalanobis distance | en_US |
dc.type | Article | en_US |
dc.relation.conference | ICT Innovations. Machine Learning and Applications | en_US |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Faculty of Computer Science and Engineering | - |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.