PowerGuard: Using Power Side-Channel Signals to Secure Motion Controllers in ICS

Abstract

Motion control systems, extensively utilized in domains like 3D printing, CNC machining, and robotic arm operations, are pivotal in modern manufacturing and automation processes. Consequently, a specific category of attacks, designed to target these systems, can manipulate the movements of controlled objects while replaying false sensor readings to evade existing tools, thereby severely disrupting these essential operations without being detected. To make things worse, the limited computing resources of embedded devices in these systems constrain the implementation of robust security protections and monitoring mechanisms locally. To solve this, we propose a novel side-channel method that leverages current signals emitted by motors to reconstruct trajectories for attack detection. In this paper, we design and implement a two-stage detection framework, dubbed PowerGuard. In the offline learning stage, PowerGuard first captures the current signals emitted by the servo motors and models the correlation between these signals and corresponding movement trajectories. In the real-time monitoring stage, PowerGuard finds outliers that deviate from the desired trajectory described in the benign G-code file. We have evaluated PowerGuard using a typical motion control system that contains CNC machine tools from different vendors (e.g., Siemens 828D, 840D-sl, Fanuc 0i-md, 0i-tf). We conducted extensive experiments to evaluate the reconstruction accuracy and attack detection performance. Experimental results show that PowerGuard can reconstruct movement trajectories with an error of 0.047mm, and detect 93.35% of various trajectory anomalies.