Analysis of Physical Access Control System for Understanding Users Behavior and Anomaly Detection Using Neo4j

Abstract

Physical Access Control (PAC) systems play a central role in the protection of critical infrastructures, where both the provision of timely access and preserving the security of sensitive areas are paramount. A key challenge is how to detect normal and abnormal behavior of authorized users in PAC systems. This problem becomes increasingly severe as such systems become more and more complex, and is deployed to manage a large amount of sensitive or private areas, buildings and resources. Therefore, the focus of this paper is to develop a Graph-based method for analysis of PAC log data to detect normal and abnormal behavior. To achieve this, we have developed an Eclipse application (AC2Neo4j) that transforms PAC log data into Neo4j automatically, thus allowing for powerful analysis to take place using cypher queries.