We present a cache attack monitoring methodology that leverages statistical machine learning models to detect n-day hardware attacks by analyzing the electromagnetic emanations of a device. Experimental results from a Raspberry Pi 4 hosting Linux and a Jetson TX2 development board running a Linux guest hosted by seL4 demonstrate that our approach can sense Spectre attacks with a concordance statistic of 97% and 95%.