Title of article :
A probabilistic risk analysis for multimodal entry control
Author/Authors :
Kalu?a، نويسنده , , Bo?tjan and Dovgan، نويسنده , , Erik and Tu?ar، نويسنده , , Tea and Tambe، نويسنده , , Milind and Gams، نويسنده , , Matja?، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2011
Pages :
9
From page :
6696
To page :
6704
Abstract :
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.
Keywords :
Entry control , Verification , Multi-layer learning , Risk analysis , Machine Learning , Behavior modeling , Data fusion
Journal title :
Expert Systems with Applications
Serial Year :
2011
Journal title :
Expert Systems with Applications
Record number :
2349368
Link To Document :
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