Abstract
Attacks in cyber-physical systems (CPS) which manipulate sensor
readings can cause enormous physical damage if undetected.
Detection of attacks on sensors
is crucial to mitigate this issue.
We study supervised regression as a means to detect anomalous sensor
readings, where each sensor's measurement is predicted as a function
of other sensors.
We show that several common learning approaches in this context
are still vulnerable to stealthy attacks, which carefully
modify readings of compromised sensors to cause desired damage while
remaining undetected.
Next, we model the interaction between the CPS defender and attacker
as a Stackelberg game in which the defender chooses detection
thresholds, while the attacker deploys a stealthy attack in response.
We present a heuristic algorithm for finding an approximately optimal threshold for
the defender in this game, and show that it increases system
resilience to attacks without significantly increasing the false alarm rate.
Keywords
- Multidisciplinary Topics and Applications: Security and Privacy
- Machine Learning Applications: Applications of Supervised Learning
