Explaining Probabilistic Bayesian Neural Networks for Cybersecurity Intrusion Detection

Abstract

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.

Keywords

• Bayesian explanation
• Bayesian autoencoder
• uncertainty quantification
• explainability
• aleatoric and epistemic uncertainties