Hardware Trojan detection through golden chip-free statistical side-channel fingerprinting

Abstract

Statistical side channel fingerprinting is a popular hardware Trojan detection method, wherein a parametric signature of a chip is collected and compared to a trusted region in a multi-dimensional space. This trusted region is statistically established so that, despite the uncertainty incurred by process variations, the fingerprint of Trojan-free chips is expected to fall within this region while the fingerprint of Trojan-infested chips is expected to fall outside. Learning this trusted region, however, assumes availability of a small set of trusted (i.e. “golden”) chips. Herein, we rescind this assumption and we demonstrate that an almost equally effective trusted region can be learned through a combination of a trusted simulation model, measurements from process control monitors (PCMs) which are typically present either on die or on wafer kerf, and advanced statistical tail modeling techniques. Effectiveness of this method is evaluated using silicon measurements from two hardware Trojan-infested versions of a wireless cryptographic integrated circuit.