Public cloud-base big alarm data analytics platform for large-scale physical security

Along with the development of ICT technology, physical and building security services are also evolved. State-of-the-art sensors detect the slightest changes in movement, heat, smoke, light, vibration, etc. Most of the on-premise servers were used to provide building care services in the existing village or small city-scale areas. In this environment, we were able to provide reliable services to customers without any problems. However, there is a problem in providing services for large cities or the entire country. There is a limit to storing and analyzing a big amount of alarm data in the on-premise server. Furthermore, the detection mechanism determines actual intrusion based on pre-defined rule without data analytics. This mechanism has high false alarm ratio and error rate. To solve this problem, this paper introduces a public cloud-based platform that provides security services by collecting, storing, and analyzing alarm data simultaneously generated across the entire country. The proposed platform improves the accuracy of false intrusion determination through machine learning in public cloud. Compared to previous rule-base algorithm, our model improves false alarm detection ratio around 20%. It uses both structured and unstructured data sets to determine false alarms. This platform supports the security guard (commander) by visualizing the analysis results using Microsoft Power BI service. In addition, we provide statistical analysis results between alarm data and related weather conditions.