A probabilistic risk analysis for multimodal entry control |
| |
| Authors: | Boštjan Kaluža Erik Dovgan Tea Tušar Milind Tambe Matjaž Gams |
| |
| Affiliation: | 1. Department of Civil and Industrial Engineering, University of Pisa, Pisa, Italy;2. Laboratory of Industrial Safety and Environmental Sustainability - DICAM, Alma Mater Studiorum - Università di Bologna, Bologna, Italy;1. Università degli Studi di Salerno, DICIV, Italy;2. Università degli Studi di Genova, DIME, Italy;3. Università degli Studi di Messina, DipIng, Italy;1. Technische Universität Kaiserslautern, Department of Mathematics, Erwin-Schrödinger-Straße, Kaiserslautern 67663, Germany;2. Fraunhofer ITWM, Fraunhoferplatz 1, Kaiserslautern 67663, Germany;1. Department of Civil and Industrial Engineering, University of Pisa, Pisa, Italy;2. Department of Mechanical and Industrial Engineering, NTNU - Norwegian University of Science and Technology, Trondheim, Norway;3. Laboratory of Industrial Safety and Environmental Sustainability - DICAM, Alma Mater Studiorum - Università di Bologna, Bologna, Italy |
| |
| 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: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
