Apparatus and method to detect power supply security attack and risk mitigation

Abstract

Hardware based unsupervised based machine-learning (ML) approach to identify a security threat to the processor (e.g., caused by probing of a power supply rail). An apparatus is provided which includes an on-die power supply droop detector as a feature extractor. The droop detector detects a droop in the power supply caused by a probe physically coupling to the power supply rail. The droop detector in combination with machine-learning logic detects change in power supply rail impedance profile due to a probe coupled to the power supply rail. A deep-neural network (DNN) is provided for feature classification that classifies a security threat from normal operation and from operations caused by aging of devices in the processor. The DNN is trained in a training phase or production phase of the processor. An aging sensor is used to distinguish classification of aged data vs. normal data and data from security attack.