GAN-Sec: Generative Adversarial Network Modeling for the Security Analysis of Cyber-Physical Production Systems
Sujit Rokka Chhetria, Anthony Bahadir Lopezb, Jiang Wanc and Mohammad Abdullah Al Faruqued
University of California, Irvine, California, USA
aschhetri@uci.edu
banthl10@uci.edu
cjiangwan@uci.edu
dalfaruqu@uci.edu
ABSTRACT
Cyber-Physical Production Systems (CPPS) will usher a new era of smart manufacturing. However, CPPS will be vulnerable to cross-domain attacks due to the interactions between the cyber and physical domains. To address the challenges of modeling cross-domain security in CPPS, we are proposing GAN-Sec, a novel conditional Generative Adversarial Network based modeling approach to abstract and estimate the relations between the cyber and physical domains. Using GAN-Sec, we are able to determine if various security requirements such as confidentiality, availability, and integrity are met. We provide a security analysis of an additive manufacturing system to demonstrate the applicability of GAN-Sec.
