Aerospace & Defense Technology - April 2024 - 33

Tech Briefs
Image: The GVR-BOt used in the experiment by UniSA and Charles Sturt AI researchers. (Image: UniSA.)
(UniSA) trained the robot's operating
system to learn the signature of a MitM
eavesdropping cyberattack. This is where
attackers interrupt an existing conversation
or data transfer.
The algorithm, tested in real time on a
replica of a U.S. Army combat ground
vehicle, was 99 percent successful in preventing
a malicious attack. False positive
rates of less than 2 percent validated the
system, demonstrating its effectiveness.
The results have been published in
IEEE Transactions on Dependable and
Secure Computing.
UniSA autonomous systems researcher,
Professor Anthony Finn, says the proposed
algorithm performs better than
other recognition techniques used
around the world to detect cyberattacks.
Professor Finn and Dr. Fendy Santoso
from Charles Sturt Artificial Intelligence
and Cyber Futures Institute collaborated
with the U.S. Army Futures Command to
replicate a man-in-the-middle cyberattack
on a GVT-BOT ground vehicle and
trained its operating system to recognize
an attack.
" The robot operating system (ROS) is
extremely susceptible to data breaches
and electronic hijacking because it is so
highly networked, " Prof Finn says.
" The advent of Industry 4, marked by
the evolution in robotics, automation,
and the Internet of Things, has demanded
that robots work collaboratively, where
sensors, actuators and controllers need to
Aerospace & Defense Technology, April 2024
communicate and exchange information
with one another via cloud services.
" The downside of this is that it makes
them highly vulnerable to cyberattacks.
" The good news, however, is that the
speed of computing doubles every couple
of years, and it is now possible to
develop and implement sophisticated AI
algorithms to guard systems against digital
attacks. "
Dr. Santoso says despite its tremendous
benefits and widespread usage, the
robot operating system largely ignores
security issues in its coding scheme due
to encrypted network traffic data and
limited integrity-checking capability.
" Owing to the benefits of deep learning,
our intrusion detection framework is
robust and highly accurate, " Dr. Santoso
says. " The system can handle large datasets
suitable to safeguard large-scale and realtime
data-driven systems such as ROS. "
Professor Finn and Dr. Santoso plan
to test their intrusion detection algorithm
on different robotic platforms,
such as drones, whose dynamics are
faster and more complex compared to a
ground robot.
This work was performed by Anthony
Finn and Fendy Santoso for the
Defence and Systems Institute (DASI),
University of South Australia. For
more information, download the
Technical Support Package (free white
paper) at mobilityengineeringtech.
com/tsp under the Sensors category.
mobilityengineeringtech.com
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Aerospace & Defense Technology - April 2024

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