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A probabilistic risk analysis for multimodal entry control
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DOI：10.1016/j.eswa.2010.11.065.png)
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Abstract
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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. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
Entry control
Verification
Risk analysis
Multi-layer learning
Machine learning
Behavior modeling
Data fusion
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