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Data‐based optimal Denial‐of‐Service attack scheduling against robust control based on Q‐learning
Author(s) -
An Liwei,
Yang GuangHong
Publication year - 2019
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4666
Subject(s) - denial of service attack , computer science , schedule , scheduling (production processes) , a priori and a posteriori , distributed computing , computer network , computer security , engineering , philosophy , the internet , epistemology , world wide web , operating system , operations management
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 anL 2framework, 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.