Abstract
This research deals with the task of detecting security anomalies and incidents in corporate smart spaces equipped with physical access control systems that support simultaneous implementation of various user identification and/or authentication methods and are often integrated with corporate cyber-physical and robotic systems. This approach allows gathering auxiliary data related to user behavior and thus detecting a wider range of security-related situation, such as entry and exit mismatches, usage of another person’s passes, and even faults and failures of the access control system itself, as well as achieving the higher reliability of user identification. For this purpose, an architectural solution for a system that uses RFID identification and face recognition was built. A corresponding data model was proposed. Using this data model and its implementation (for example, using relational databases and object-relationship mappings), the gathered data can be processed in order to detect potentially anomalous situations and security incidents. Then, the description and classification of such situations was given, and the delays of operation were measured during the experiment. The measurement shows that the delays allow experiencing the process of interaction as being one continuous flow. The tasks of future research were also specified.
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Levonevskiy, D., Motienko, A., Vinogradov, M. (2022). Complex User Identification and Behavior Anomaly Detection in Corporate Smart Spaces. In: Ronzhin, A., Meshcheryakov, R., Xiantong, Z. (eds) Interactive Collaborative Robotics. ICR 2022. Lecture Notes in Computer Science, vol 13719. Springer, Cham. https://doi.org/10.1007/978-3-031-23609-9_18
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