Using Machine Learning for Side Channel Analysis

Side channel analysis involves using externally recorded signals from a device (such as electromagnetic radiation or power consumption) to determine what the device is preforming. Our current work involves using this avenue of data in conjunction with machine learning techniques to accomplish two tasks. First is anomaly detection, in which a model takes as input a side channel signal and outputs a determination of whether or not the device is running software that the model has been trained on, or if it is running “anomalous” software. The second task is instruction level tracking, where a model is trained to recognize “jump” commands within code, and then mark in a recording where those jump commands occur.