Anomaly Detection in Embedded Systems Using Power and Memory Side Channels

Abstract

We propose multi-modal anomaly detection in embedded systems using time-correlated measurements of power consumption and memory accesses. Time series of power consumption of the processor and memory accesses between L2 cache and memory bus under known-good conditions are used to train one-class support vector machine (SVM) and isolation forest classifiers. These side channels have complementary anomaly detection capabilities. Experiments on a high-fidelity processor emulator show that the method accurately detects anomalies.