Explaining Probabilistic Bayesian Neural Networks for Cybersecurity Intrusion Detection

The probabilistic Bayesian neural network(BNN) is good at providing trustworthy outcomes that is important, e.g. in intrusion detection. Due to the complex of probabilistic BNN, it is looks like a "black box". The explanation of its prediction is needed for improving its transparency. Howe...

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Veröffentlicht in:IEEE access Jg. 12; S. 97004 - 97016
Hauptverfasser: Yang, Tengfei, Qiao, Yuansong, Lee, Brian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2024
Schlagworte:
aleatoric and epistemic uncertainties
Bayes methods
Bayesian autoencoder
Bayesian explanation
Computer security
Data models
explainability
Intrusion detection
Predictive models
Probabilistic logic
Uncertainty
uncertainty quantification
ISSN:2169-3536, 2169-3536
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Zusammenfassung:The probabilistic Bayesian neural network(BNN) is good at providing trustworthy outcomes that is important, e.g. in intrusion detection. Due to the complex of probabilistic BNN, it is looks like a "black box". The explanation of its prediction is needed for improving its transparency. However, there is no explanatory method to explain the prediction of probabilistic BNN for the reason of uncertainty. For enhance the explainability of BNN model concerning uncertainty quantification, this paper proposes a Bayesian explanatory model that accounts for uncertainties inherent in Bayesian Autoencoder, encompassing both aleatory and epistemic uncertainties. Through global and local explanations, this Bayesian explanatory model is applied to intrusion detection scenarios. Fidelity and sensitivity analyses showcase that the proposed Bayesian explanatory model, which incorporates external uncertainty, effectively identifies key features and provides robust explanations.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3421330