Intelligent Risk Analysis in Access Control

Abstract

Access control is an important security activity that prevents undesired persons from entering secure buildings or perimeters. The advanced risk anal-ysis presented in this paper enables distinguishing between acceptable and undesired 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 learning and meta-learning modules. The obtained results indi-cate an important increase in detecting security at-tacks.