Towards trustworthy cybersecurity operations using Bayesian Deep Learning to improve uncertainty quantification of anomaly detection

Uncertainty quantification of cybersecurity anomaly detection results provides critical guidance for decision makers on whether or not to accept the results. Improving the trustworthiness of anomaly predictions can reduce the amount of alert false positives that security teams have to process. In th...

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Bibliographic Details

Published in:	Computers & security Vol. 144; p. 103909
Main Authors:	Yang, Tengfei, Qiao, Yuansong, Lee, Brian
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.09.2024
Subjects:	Anomaly probability Bayesian autoencoder Cybersecurity Heteroscedastic noise Uncertainty quantification Uncertainty quantification Cybersecurity Bayesian autoencoder Heteroscedastic noise Anomaly probability
ISSN:	0167-4048
Online Access:	Get full text
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