Large Scale Automated Forecasting for Monitoring Network Safety and Security
This addresses the problem of monitoring network safety and security for organizations handling large-scale streaming data, though it appears incremental as it builds on existing forecasting methods.
The authors tackled the challenge of real-time large-scale streaming data forecasting for safety and security monitoring by developing an automated, scalable system that provides short- and long-term forecasts to detect issues in internet-connected devices in advance.
Real time large scale streaming data pose major challenges to forecasting, in particular defying the presence of human experts to perform the corresponding analysis. We present here a class of models and methods used to develop an automated, scalable and versatile system for large scale forecasting oriented towards safety and security monitoring. Our system provides short and long term forecasts and uses them to detect safety and security issues in relation with multiple internet connected devices well in advance they might take place.