PID-Piper: Recovering Robotic Vehicles from Physical Attacks
Pritam Dash, Guanpeng Li, Zitao Chen, Mehdi Karimibiuki, Karthik Pattabiraman
Abstract
Robotic Vehicles (RV) rely extensively on sensor inputs to operate autonomously. Physical attacks such as sensor tampering and spoofing can feed erroneous sensor measurements to deviate RVs from their course and result in mission failures. In this paper, we present PID-Piper, a novel framework for automatically recovering RVs from physical attacks. We use machine learning (ML) to design an attack resilient Feed-Forward Controller (FFC), which runs in tandem with the RV's primary controller and monitors it. Under attacks, the FFC takes over from the RV's primary controller to recover the RV, and allows the RV to complete its mission successfully. Our evaluation on 6 RV systems including 3 real RVs shows that PID-Piper achieves high accuracy in emulating the RV's controller, in the absence of attacks, with no false positives. Further, PID-Piper allows RVs to complete their missions successfully despite attacks in 83% of the cases, while incurring low performance overheads.