  Because of the threat of advanced multi-step attacks, it is often difficult
for security operators to completely cover all vulnerabilities when deploying
remediations. Deploying sensors to monitor attacks exploiting residual
vulnerabilities is not sufficient and new tools are needed to assess the risk
associated to the security events produced by these sensors. Although attack
graphs were proposed to represent known multi-step attacks occurring in an
information system, they are not directly suited for dynamic risk assessment.
In this paper, we present the Bayesian Attack Model (BAM), a Bayesian
network-based extension to topological attack graphs, capable of handling
topological cycles, making it fit for any information system. Evaluation is
performed on realistic topologies to study the sensitivity of its probabilistic
parameters.
