Abdullah T. Al-Essa

Behavior-Aware Access Control for IoT Networks Using Lightweight Machine Learning at the Gateway Level

A behavior-sensitive access control solution is presented, which integrates lightweight supervised Machine Learning on the IoT gateway to provide dynamic authorization and uses a Supervised Random Forest model to process real-time statistical feature summaries in terms of mean, standard deviation, and sparsity of the IoT telemetry data.