PRIMER: Profiling Interrupts using Electromagnetic Side-Channel for Embedded Devices

Abstract

Recent proliferation of CPS and IoT devices has led to an increasing demand for analyzing performance and timing of event-driven computational activity, especially interrupts and exceptions. However, these devices typically lack hardware resources, power, and system-software infrastructure for profiling/monitoring such events. Even when feasible, the profiling/monitoring activity itself can perturb the performance and timing of the timing-sensitive activity to be analyzed, therefore producing misleading results. Thus, we present PRIMER, a novel approach for profiling interrupts. PRIMER leverages existing unintentional (side-channel) electromagnetic emanations of the profiled/monitored device to identify its asynchronous execution (e.g., interrupt handlers). PRIMER leaves the monitored system (and its behavior) completely unchanged, requires no system resources or support, and introduces neither overheads nor perturbation in the monitored system. We validate PRIMER by analyzing signals that correspond to five different types of interrupts on an IoT device (ARM Cortex-M), achieving 99.5% accuracy (with no false positives), and on an MSP430 microcontroller-based device with even better accuracy. We also demonstrate the effectiveness of PRIMER in analyzing page faults and network interrupts when executing real-world applications on a more sophisticated embedded device (ARM Cortex-A8), and show that the results provided by PRIMER can provide useful insights about an application's interaction with the system's virtual memory and network-oriented services.