Dynamic Risk Assessment by Bayesian Attack Graphs and Process Mining
While attack graphs are useful for identifying major cybersecurity threats affecting a system, they do not provide operational support 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. Specifically, the proposed approach applies process mining techniques to characterize malicious network traffic and derive evidence regarding the probability of having a vulnerability actively exploited. This evidence is then provided to a BAG, which updates its conditi...
| Authors | Francesco Vitale, Simone Guarino, Stefano Perone et al. |
|---|---|
| Primary category | cs.CR |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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