Data‐based optimal Denial‐of‐Service attack scheduling against robust control based on Q‐learning. (10th July 2019)

• Record Type: Journal Article
• Authors: An, Liwei; Yang, Guang‐Hong
• Other Names: Quevedo Daniel E. guestEditor.; Chatterjee Debasish guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 29:Number 15(2019)
• Page Start: 5178
• Page End: 5194
• Publication Date: 2019-07-10
• Subjects: Cyber‐physical systems -- DoS attacks -- multichannel transmission -- Q‐learning -- robust control; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.4666
• Languages: English
• ISSNs: 1049-8923

Abstract

Summary: Attack optimization is an important issue in securing cyber‐physical systems. This paper investigates how an attacker should schedule its denial‐of‐service attacks to degrade the robust performance of a closed‐loop system. The measurements of system states are transmitted to a remote controller over a multichannel network. With limited resources, the attacker only has the capacity to jam sparse channels and to decide which channels should be attacked. Under an L 2 framework, a data‐based optimal attack strategy that uses Q‐learning is proposed to maximize the effect on the closed‐loop system. The Q‐learning algorithm can adaptively learn the optimal attack using data sniffed over the wireless network without requiring a priori knowledge of system parameters. Simulation results sustain the performance of the proposed attack scenario.