Dynamic Risk Assessment by Bayesian Attack Graphs and Process Mining

ArXi:2604.18080v1 Announce Type: cross While attack graphs are useful for identifying major cybersecurity threats affecting a system, they do not provide operational for determining the likelihood of having a known vulnerability exploited, or that critical system nodes are likely to be compromised. In this paper, we perform dynamic risk assessment by combining Bayesian Attack Graphs (BAGs) and online monitoring of system behavior through process mining.