RF Analog Hardware Trojan Detection Through Electromagnetic Side-Channel

Abstract

With the advent of globalization, hardware trojans provide an ever-present threat to the security of devices. Much of the research to date has centered around documenting and providing detection methods for digital trojans. Few, however, have explored the space of trojans in the RF/analog front end. Two hardware trojans, an analytical analysis of the trojan impacts on two different types of amplifiers, and an unsupervised ML detection method for edge IOT applications using magnetic tunnel junction sensors for side-channel monitoring are explored. A classification autoencoder for anomaly detection is presented with an accuracy of greater than 90% with both single tone and BLE data is presented.