Supervising Communication SoC for Secure Operation Using Machine Learning

Abstract

Manufacturers normally buy and/or fabricate communication chips using third-party suppliers, which are then integrated into a complex hardware-software stack with a variety of potential vulnerabilities. This work proposes a compact supervisory circuit to classify the operation of a Bluetooth (BT) SoC at low frequencies by monitoring the input power and radio frequency (RF) output of the BT chip passed through an envelope detector. The idea is to inexpensively fabricate an envelope detector, power supply current monitor, and classification algorithm on a custom low-frequency integrated circuit in a trusted legacy technology. When the supervisory circuit detects unexpected behavior, it can shut off power to the BT SoC. In this preliminary work, we proto-type the supervisory circuit using off-the-shelf components. We extract simple yet descriptive features from the envelope of the RF output signal. Then, we train machine learning (ML) models to classify different BT operation modes, such as BT advertising and transmit modes. Our results show ∼100% classification accuracy.