  Cooperative Adaptive Cruise Control (CACC) is a fundamental connected vehicle
application that extends Adaptive Cruise Control by exploiting
vehicle-to-vehicle (V2V) communication. CACC is a crucial ingredient for
numerous autonomous vehicle functionalities including platooning, distributed
route management, etc. Unfortunately, malicious V2V communications can subvert
CACC, leading to string instability and road accidents. In this paper, we
develop a novel resiliency infrastructure, RACCON, for detecting and mitigating
V2V attacks on CACC. RACCON uses machine learning to develop an on-board
prediction model that captures anomalous vehicular responses and performs
mitigation in real time. RACCON-enabled vehicles can exploit the high
efficiency of CACC without compromising safety, even under potentially
adversarial scenarios. We present extensive experimental evaluation to
demonstrate the efficacy of RACCON.

Cryptography and Security;Robotics;Systems and Control;Systems and Control