Internet of Predictable Things (IoPT) Framework to Increase Cyber-Physical System Resiliency
This addresses cybersecurity risks in cyber-physical systems like power grids, but appears incremental as it builds on existing digitalization frameworks.
The paper tackles the vulnerability of modern power systems to cybersecurity risks by proposing the Internet of Predictable Things (IoPT) framework, which uses advanced data analytics and machine learning to increase resiliency, demonstrated through a proof of concept testbed under various cyber attack scenarios.
During the last two decades, distributed energy systems, especially renewable energy sources (RES), have become more economically viable with increasing market share and penetration levels on power systems. In addition to decarbonization and decentralization of energy systems, digitalization has also become very important. The use of artificial intelligence (AI), advanced optimization algorithms, Industrial Internet of Things (IIoT), and other digitalization frameworks makes modern power system assets more intelligent, while vulnerable to cybersecurity risks. This paper proposes the concept of the Internet of Predictable Things (IoPT) that incorporates advanced data analytics and machine learning methods to increase the resiliency of cyber-physical systems against cybersecurity risks. The proposed concept is demonstrated using a cyber-physical system testbed under a variety of cyber attack scenarios as a proof of concept (PoC).