Authors:
Andrew Sundstrom
;
Damas Limoge
;
Vadim Pinskiy
and
Matthew Putman
Affiliation:
Nanotronics Imaging, Brooklyn, NY, U.S.A.
Keyword(s):
Cyberattack, Malicious Attack, Man-in-the-Middle, Stuxnet, Statistical Process Control, Machine Learning, Artificial Intelligence, Innovation Error, Deep Reinforcement Learning.
Abstract:
Sophisticated industrial cyberattacks focus on machine level operating systems to introduce process variations that are undetected by conventional process control, but over time, are detrimental to the system. We propose a novel approach to industrial security, by treating suspect malicious activity as a process variation and correcting for it by actively tuning the operating parameters of the system. As threats to industrial systems increase in number and sophistication, conventional security methods need to be overlaid with advances in process control to reinforce the system as a whole.