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Chapter Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture
Thomopoulos, Stelios C.A.

Chapter Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture

Author / Organization:
Humanities & Social Sciences Published 2021 Verification Pending

Overview & Synopsis

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.

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Document Information

Document Title Chapter Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture
Author / Organization Thomopoulos, Stelios C.A.
Subject & Discipline risk assessment, security, anomaly detection, deep learning, neural networks, crowd simulation, control and command, surveillance, risk-based security
Categories Humanities & Social Sciences
Publication Year 2021
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website OAPEN Library (www.oapen.org)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-298465
Original Source Link Visit Source Page on www.oapen.org »