Security state monitoring method for perception node in the power internet of things based on a low rank model

Abstract

To overcome the problem of low precision and recall in the current power internet of things security monitoring results, a low rank model based security monitoring method for power internet of things sensor nodes is proposed. This method constructs the security monitoring platform of the power internet of things sensing node, designs the adaptive sensing mechanism of edge node data types under counting bloom filter, and realises the adaptive recognition of sensing node data fields. The normal observation data is described according to the low rank part, and the abnormal data is described according to the sparse part. The augmented Lagrangian method is used to optimise the objective equation and realise anomaly detection. The experimental results show that the method has high accuracy and recall, and reliability.