**4. Conclusions**

In this chapter we discussed the concept of risk-based security, the possible trade-off between increased convenience for passengers from risk-based security and the delays induced by additional checks needed for establishing each

*Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture DOI: http://dx.doi.org/10.5772/intechopen.96209*

passenger's risk. We also presented a number of technologies, systems and applications that can be used for assessing risk at an airport or BCP without inducing additional delay as the discussed approaches estimate risk on-the-fly while passengers either walk around the airport or BCP from entrance to security check points or BCPs, or queue up in a security line awaiting to go through security checks. All methods discussed are GDPR and ethics compliant, thus they can be implemented in accordance to privacy and ethics regulations. Furthermore, the novel system architecture for Security and Safety monitoring systems introduced in [3] has been presented. The proposed system aims to identify adverse events or behaviors which may endanger the safety of people or their well-being having the ability to adapt in the surveillance environment changes. The dynamic adjustment of the algorithmic parameters adopted in various units of the system such as intelligence, and Risk assessment, makes it possible to monitor security threats as they evolve. Thus, the proposed scheme provides the potential of a high-performance system both in terms of the detection interval as well as in terms of the performance accuracy offering the capability of a timely and efficient response to abnormal events and behaviors.
