Power Side Channel Attack Analysis and Detection

Side Channel Attack (SCA) is a serious threat to the hardware implementation of cryptographic protocols. Various side channels such as, power, timing, electromagnetic emission and acoustic noise have been explored to extract the secret keys. Machine Learning (ML)-based detection of SCA have been proposed in past which incur high design overheads and, require digitization that reduce their accuracy under process variations. We propose a real-time power SCA detection technique using on-chip sensors based on a thorough analysis. The dependency of phase/frequency of Ring Oscillator (RO) on supply voltage is exploited to detect the insertion of a SCA resistance in the power rail. The proposed approach is validated using simulation with a detailed model of Power Delivery Network (PDN) and power grid. The technique can detect a minimum resistance of 1 Ω within 2 μs of attack initiation and incurs a tiny fraction of area/power (0.044%/0.1065%, respectively) compared to ML-based techniques.