TechRxiv
TSMC_Automated_attack_Generation_Submitted_10_21_21.pdf (985.23 kB)
Download file

Algorithm Design for Resilient Cyber-Physical Systems using an Automated Attack Generative Model

Download (985.23 kB)
preprint
posted on 29.11.2021, 03:54 by Yu Zheng, Ali Sayghe, Olugbenga AnubiOlugbenga Anubi
This paper presents a suite of algorithms for detecting and localizing attacks in cyber-physical systems, and performing improved resilient state estimation through a pruning algorithm. High performance rates for the underlying detection and localization algorithms are achieved by generating training data that cover large region of the attack space. An unsupervised generative model trained by physics-based discriminators is designed to generate successful false data injection attacks. Then the generated adversarial examples are used to train a multi-class deep neural network which detects and localizes the attacks on measurements. Next, a pruning algorithm is included to improve the precision of localization result and provide performance guarantees for the resulting resilient observer. The performance of the proposed method is validated using the numerical simulation of a water distribution cyber-physical system.

Funding

U.S. Department of Energy’s Office of Cybersecurity, Energy Security, AND Emergency Response (CESER) under the award number DE-CR0000005

History

Email Address of Submitting Author

oanubi@fsu.edu

ORCID of Submitting Author

0000-0002-8662-6076

Submitting Author's Institution

Florida State University

Submitting Author's Country

United States of America