Chapter Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture
Risk-based security is a concept introduced in order to provide security checks without inconveniencing travelers that are being checked with unqualified scrutiny checks while maintaining the same level of security with current check point practices without compromising security standards. Furthermo...
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| Định dạng: | Online |
| Ngôn ngữ: | Tiếng Anh |
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InTechOpen
2021
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| Những chủ đề: | |
| Truy cập trực tuyến: | ONIX_20210602_10.5772/intechopen.96209_505 |
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| _version_ | 1869527295845203968 |
|---|---|
| author | Thomopoulos, Stelios C.A. |
| author_browse | Thomopoulos, Stelios C.A. |
| author_facet | Thomopoulos, Stelios C.A. |
| author_sort | Thomopoulos, Stelios C.A. |
| collection | Directory of Open Access Books |
| description | Risk-based security is a concept introduced in order to provide security checks without inconveniencing travelers that are being checked with unqualified scrutiny checks while maintaining the same level of security with current check point practices without compromising security standards. Furthermore, risk-based security, as a means of improving travelers’ experience at check points is expected to reduce queueing and waiting times while improving at the same travelers’ experience during checks. A number of projects have been funded by the European Commission to investigate the concept of risk-based security and develop the means and technology required to implement it. The author is the Coordinator of two of the flagship projects funded by EC on risk-based security: FLYSEC and TRESSPASS. This chapter discusses and analyses the concept of risk-based security, the inherent competing mechanism between risk assessment, screening time and level of security, and means to implement risk-based security based on anomaly detection using deep learning and artificial intelligence (AI) methods. |
| format | Online |
| id | doab-20.500.12854ir-70615 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | InTechOpen |
| publisherStr | InTechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-706152025-08-13T14:12:00Z Chapter Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture Thomopoulos, Stelios C.A. risk assessment, security, anomaly detection, deep learning, neural networks, crowd simulation, control and command, surveillance, risk-based security thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology Risk-based security is a concept introduced in order to provide security checks without inconveniencing travelers that are being checked with unqualified scrutiny checks while maintaining the same level of security with current check point practices without compromising security standards. Furthermore, risk-based security, as a means of improving travelers’ experience at check points is expected to reduce queueing and waiting times while improving at the same travelers’ experience during checks. A number of projects have been funded by the European Commission to investigate the concept of risk-based security and develop the means and technology required to implement it. The author is the Coordinator of two of the flagship projects funded by EC on risk-based security: FLYSEC and TRESSPASS. This chapter discusses and analyses the concept of risk-based security, the inherent competing mechanism between risk assessment, screening time and level of security, and means to implement risk-based security based on anomaly detection using deep learning and artificial intelligence (AI) methods. 2021-02-10T12:58:18Z 2021-06-02T10:13:50Z 2021 chapter ONIX_20210602_10.5772/intechopen.96209_505 https://library.oapen.org/handle/20.500.12657/49391 https://directory.doabooks.org/handle/20.500.12854/70615 eng open access image/jpeg image/jpeg image/jpeg n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/49391/1/75329.pdf https://library.oapen.org/bitstream/20.500.12657/49391/1/75329.pdf https://library.oapen.org/bitstream/20.500.12657/49391/1/75329.pdf InTechOpen 10.5772/intechopen.96209 10.5772/intechopen.96209 035ecc65-6737-43cf-a13a-6bdf67ce01f4 open access |
| spellingShingle | risk assessment, security, anomaly detection, deep learning, neural networks, crowd simulation, control and command, surveillance, risk-based security thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology Thomopoulos, Stelios C.A. Chapter Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture |
| title | Chapter Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture |
| title_full | Chapter Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture |
| title_fullStr | Chapter Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture |
| title_full_unstemmed | Chapter Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture |
| title_short | Chapter Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture |
| title_sort | chapter risk assessment and automated anomaly detection using a deep learning architecture |
| topic | risk assessment, security, anomaly detection, deep learning, neural networks, crowd simulation, control and command, surveillance, risk-based security thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology |
| topic_facet | risk assessment, security, anomaly detection, deep learning, neural networks, crowd simulation, control and command, surveillance, risk-based security thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology |
| url | ONIX_20210602_10.5772/intechopen.96209_505 |
| work_keys_str_mv | AT thomopoulossteliosca chapterriskassessmentandautomatedanomalydetectionusingadeeplearningarchitecture |