Entry control is an important security measure that prevents undesired persons from entering secure areas.
The advanced risk analysis presented in this paper makes it possible to distinguish between acceptable and
unacceptable entries, based on several entry sensors, such as fingerprint readers, and intelligent methods that
learn behavior from previous entries. We have extended the intelligent layer in two ways: first, by adding
a meta-learning layer that combines the output of specific intelligent modules, and second, by constructing
a Bayesian network to integrate the predictions of the learning and meta-learning modules. The obtained
results represent an important improvement in detecting security attacks.