  Security is of primary importance to vehicles. The viability of performing
remote intrusions onto the in-vehicle network has been manifested. In regard to
unmanned autonomous cars, limited work has been done to detect intrusions for
them while existing intrusion detection systems (IDSs) embrace limitations
against strong adversaries. In this paper, we consider the very nature of
autonomous car and leverage the road context to build a novel IDS, named Road
context-aware IDS (RAIDS). When a computer-controlled car is driving through
continuous roads, road contexts and genuine frames transmitted on the car's
in-vehicle network should resemble a regular and intelligible pattern. RAIDS
hence employs a lightweight machine learning model to extract road contexts
from sensory information (e.g., camera images and distance sensor values) that
are used to generate control signals for maneuvering the car. With such ongoing
road context, RAIDS validates corresponding frames observed on the in-vehicle
network. Anomalous frames that substantially deviate from road context will be
discerned as intrusions. We have implemented a prototype of RAIDS with neural
networks, and conducted experiments on a Raspberry Pi with extensive datasets
and meaningful intrusion cases. Evaluations show that RAIDS significantly
outperforms state-of-the-art IDS without using road context by up to 99.9%
accuracy and short response time.
