Abstract
In this paper, we present a novel approach of symmetrical pruning for lightweight anomaly detectors based on an autoencoder, leveraging the unique encoder-decoder structure of the autoencoder. We develop an efficient network anomaly detector with reduced computational overhead by computing the reconstruction error between hidden activations of an input and its hidden reconstructions and symmetrically pruning nodes with high error values.
| Original language | English |
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| Title of host publication | MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 634-635 |
| Number of pages | 2 |
| ISBN (Electronic) | 9798400705816 |
| DOIs | |
| State | Published - 3 Jun 2024 |
| Event | 22nd Annual International Conference on Mobile Systems, Applications and Services, MOBISYS 2024 - Minato-ku, Japan Duration: 3 Jun 2024 → 7 Jun 2024 |
Publication series
| Name | MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services |
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Conference
| Conference | 22nd Annual International Conference on Mobile Systems, Applications and Services, MOBISYS 2024 |
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| Country/Territory | Japan |
| City | Minato-ku |
| Period | 3/06/24 → 7/06/24 |
Bibliographical note
Publisher Copyright:© 2024 Copyright is held by the owner/author(s).
Keywords
- anomaly detection
- autoencoder
- lightweight
- pruning
- symmetry