Defending Industrial Production Using AI Process Control
Damas W. Limoge, Andrew Sundstrom, Vadim Pinskiy, Matthew Putman · 2020
Cyberattacks have grown more nuanced and sophisticated in recent years, in part to meet the growing complexity of the systems they are designed to compromise or destroy. The new breed of cyberattacks are decidedly systemic, affecting more than a single node or a single point of failure, to better hide and time-integrate its malicious programming. Current modes of intrusion detection and correction in an industrial setting are based on a statistical process control scheme that unfolded in the mid-Twentieth Century and which, while still effective for diagnosing pronounced, single-node malicious behavior, is ill-suited for the properties of modern, sophisticated cyberattacks. We propose a novel approach, based on deep reinforcement learning, that treats malicious behavior as a process variation and corrects for it by actively tuning the operating parameters of the system. In this way, it can be layered atop, and functionally complement, standard statistical process control. We describe our approach in the additive manufacturing setting of 3D printing and explain how it can scale to large systems composed of many nodes in a complex topology. We argue an overlay of AI process control facilitates whole-system protection against modern cyberattacks.