We consider a prototypical intelligent transportation
system with a control law that is specifically designed to avoid
collisions. We experimentally demonstrate that, nevertheless, an
attack on a position sensor can result in collisions between
vehicles. This is a consequence of the feeding of malicious
sensor measurements to the controller and the collision avoidance
module built into the system. This is an instance of the
broader concern of cybersecurity vulnerabilities opened up by
the increasing integration of critical physical infrastructures with
the cyber system. We consider a solution based on “dynamic
watermarking” of signals to detect and stop such attacks on
cyber-physical systems. We show how dynamic watermarking
can handle nonlinearities arising in vehicular models. We then
experimentally demonstrate that employing this nonlinear extension
indeed restores the property of collision freedom even in the
presence of attacks.
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This content will become publicly available on December 5, 2024
Catch You if Pay Attention: Temporal Sensor Attack Diagnosis Using Attention Mechanisms for Cyber-Physical Systems
In Cyber-Physical Systems (CPS), sensor data integrity is crucial since acting on malicious sensor data can cause serious consequences, given the tight coupling between cyber components and physical systems. While extensive works focus on sensor attack detection, attack diagnosis that aims to find out when the attack starts has not been well studied yet. This temporal sensor attack diagnosis problem is equally important because many recovery methods rely on the accurate determination of trustworthy historical data. To address this problem, we propose a lightweight data-driven solution to achieve real-time sensor attack diagnosis. Our novel solution consists of five modules, with the attention and diagnosis ones as the core. The attention module not only helps accurately predict future sensor measurements but also computes statistical attention scores for the diagnosis module. Based on our unique observation that the score fluctuates sharply once an attack launches, the diagnosis module determines the onset of an attack through monitoring the fluctuation. Evaluated on high-dimensional high-fidelity simulators and a testbed, our solution demonstrates robust and accurate temporal diagnosis results while incurring millisecond-level computational overhead on Raspberry Pi.
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- Award ID(s):
- 2333980
- PAR ID:
- 10499417
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Real-Time Systems Symposium (RTSS)
- ISBN:
- 979-8-3503-2857-8
- Page Range / eLocation ID:
- 64 to 77
- Format(s):
- Medium: X
- Location:
- Taipei, Taiwan
- Sponsoring Org:
- National Science Foundation
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