Auto-encoding Robot State against Sensor Spoofing Attacks

In robotic systems, the physical world is highly coupled
with cyberspace. New threats affect cyber-physical systems
as they rely on several sensors to perform critical operations.
The most sensitive targets are their location systems, where
spoofing attacks can force robots to behave incorrectly. In this
paper, we propose a novel anomaly detection approach for
sensor spoofing attacks, based on an auto-encoder architecture.
After initial training, the detection algorithm works directly on
the compressed data by computing the reconstruction errors.
We focus on spoofing attacks on Light Detection and Ranging
(LiDAR) systems. We tested our anomaly detection approach
against several types of spoofing attacks comparing four different
compression rates for the auto-encoder. Our approach has a 99%
True Positive rate and a 10% False Negative rate for the 83%
compression rate. However, a compression rate of 41% could
handle almost all of the same attacks while using half the data.