Machine learning at the network edge for automated home intrusion monitoring

Abstract

Monitoring of residences and businesses can be effectively performed using machine learning algorithms. As sensors and devices used for monitoring become more complex, having humans process the information to detect intrusions would be expensive and difficult to scale. We propose an automated home/business monitoring system which resides on edge servers performing online learning on streaming data coming from homes and businesses in the neighborhood. The edge servers run Open-NetVM, a Network Function Virtualization (NFV) platform, and host multiple machine learning applications instantiated on demand. This enables us to serve a set of customers in the neighborhood on a timely basis, permitting customization and learning of the behavior of each home. We combine the results of the multiple classifiers, with each classifier examining a distinct feature related to a distinct sensor, to finally infer whether the entry is a normal one or an intrusion. Our results show that our system is able to classify intrusions better than basing the decision on a single classifier, thus reducing false alarms. We have also shown that our system can effectively scale and monitor thousands of homes.