… attack graphs and the creation of BAGs. In Section IV, we present the types of structure learning
… Algorithm the construct prior BAG and to learn structure is introduced in Section V. Finally…

… forecasting problem, Bayesian learning will instead … Bayesian learning can still improve
load forecasting models’ robustness. Such improved robustness even holds for various attacking …

… non-Bayesian learning over multi-… learn the true state out of m alternatives. We focus on
the impact of adversarial agents on the performance of consensus-based non-Bayesian learning…

… the practical cross-site scripting (XSS) attack detection to verify the validity of our algorithm.
… metrics and add the practical application of our proposed method in XSS attack detection. …

… Situations where Bayesian networks provide the natural tools for analysis are, for … Bayesian
networks as a tool for assessing intrusion evidence and whether a network is under attack) …

… Thus, given some data, we associate an attacking model with a probability distribution
over attacks which encodes our uncertainty about how the adversary will act when seeing a …

… to learn new attack strategies. As a future work, we plan to revise the method in such a way
that it can learn new attack strategies dynamically by adapting the alert Bayesian network …

… learning models robust to adversarial attacks is still an open problem. In this article, we analyse
the geometry of adversarial attacks in the over-parameterized limit for Bayesian … attacks …

… In the following, we will have a brief introduction to the Bayesian networks first. Then, we
will describe the attack pattern recognition component and alert correlation component in …

… attacks in the large-data, overparametrized limit for Bayesian Neural Networks (BNNs). We
show that, in the limit, vulnerability to gradient-based attacks … -based adversarial attacks. …