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

• Main Author: Thomopoulos, Stelios C.A (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: InTechOpen 2021
• Subjects: Computing & information technology; risk assessment, security, anomaly detection, deep learning, neural networks, crowd simulation, control and command, surveillance, risk-based security
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.