Monitoring Software Execution Flow Through Power Consumption and Dynamic Time Warping

Abstract

This work presents a technique for non-intrusive code execution tracking using side-channel signals of power consumption. Using a nearest-neighbor classifier that integrates the Dynamic Time Warping distance with information from the Control Flow Graph, it is possible to identify executed basic blocks from a trace of power consumption that exhibits temporal distortions due to assembly-level artifacts and varying operational conditions. Experimental results show that the proposed technique achieves over 95% precision when inferring the runtime execution flow of a cruise control application using unmarked traces of power consumption collected from different processors.