Exploring the influence of the choice of prior of the Variational Auto-Encoder on cybersecurity anomaly detection
ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security
Article No.: 67, Pages 1 - 9
Abstract
The Variational Auto-Encoder (VAE) is a popular generative model as the variance inference in the latent layer, the prior is an important element to improve inference efficient. This research explored the prior in the VAE by comparing the Normal family distributions and other location-scale family distributions in three aspects (performance, robustness, and complexity) in order to find a suitable prior for cybersecurity anomaly detection. Suitable distributions can improve the detection performance, which was verified at UNSW-NB15 and CIC-IDS-2017.
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- Exploring the influence of the choice of prior of the Variational Auto-Encoder on cybersecurity anomaly detection
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Published In
July 2024
2032 pages
ISBN:9798400717185
DOI:10.1145/3664476
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Published: 30 July 2024
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- Technological University of the Shannon
- Hormeon Europe Framework Program
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ARES 2024
ARES 2024: The 19th International Conference on Availability, Reliability and Security
July 30 - August 2, 2024
Vienna, Austria
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