Abstract
Recently, public attention is thoroughly aroused as to the security threats of Wireless Network Control System (WNCS), which can seriously disrupt the system operation. In order to achieve the attack effect that each sensor is damaged and maximize the terminal estimation error covariance, it is necessary to study an attack system from the attacker’s perspective. In this paper, we establish an attack system, which includes: the multi-sensor importance evaluation model, the time allocation of jamming attack, and the attack rules. Specifically, we firstly establish the wireless network control system model and the jamming attack model. Then, according to the transmission data and channel parameter information which is intercepted by the attackers, we establish an evaluation model of sensor based on the Mean Impact Value (MIV) algorithm. Then, based on the evaluation results of each sensor, we establish a distribution model of the number of attacks on each sensor. Then, we perform two jamming attack rules(continuous attack rule and good-sensor-late-attack rule)to attack each sensor. Finally, we use the attack system to conduct digital simulation experiments in first-order and high-order system. There is no different between the MIV-based sensor evaluation method in the multi-sensor importance evaluation experiment and sensor performance evaluation based on estimation error. In the jamming attack time allocation experiment, effect that every sensor was attacked had been achieved. In the attack rule experiment, we compare the experimental results of “continuous attack” and “ discontinuous attack”, and the result shows that the effect of “continuous attack” is better than that of “intermittent attack”. Similarly, we have conducted comparative experiments on all attack strategies, and the results show that “ good-sensor-late-attack ” strategy has the best effect. The effectiveness of the attack system is proved by digital simulation experiment.
Highlights
INTRODUCTIONWireless networked control systems (WNCS) are defined as a spatial distributed system which connects sensors, remote estimators and controllers by wireless communication network [1], [2].With the rapid development of Internet technology, WNCS have been extensively used, such as smart grid, smart logistics, smart transportation and smart home [3], [4]
Wireless networked control systems (WNCS) are defined as a spatial distributed system which connects sensors, remote estimators and controllers by wireless communication network [1], [2].With the rapid development of Internet technology, WNCS have been extensively used, such as smart grid, smart logistics, smart transportation and smart home [3], [4].The associate editor coordinating the review of this manuscript and approving it for publication was Byung-Seo Kim .At present, WNCSs have been increasingly important in industrial systems
The attack system consists of three layers: the multi-sensor importance evaluation based on CMIV model, the time allocation of Jamming attack and attack rules
Summary
Wireless networked control systems (WNCS) are defined as a spatial distributed system which connects sensors, remote estimators and controllers by wireless communication network [1], [2].With the rapid development of Internet technology, WNCS have been extensively used, such as smart grid, smart logistics, smart transportation and smart home [3], [4]. Wen: Optimal Jamming Attack System Against Remote State Estimation in WNCSs space does not hide-time does not hide attacks, such as DoS attacks, general replay attacks, etc. In order to maximize the terminal estimated error covariance, the purpose of this article is to design an optimal attack system from the attacker’s point of view based on the different importance of each sensor under the constraints of the attacker. (1) We firstly establish a complete jamming attack system, including multi-sensor importance evaluation model, the time allocation of jamming attack and two attack rules. Where k ∈ Z is a discrete time series, x(k) ∈ Rnx is the state value of the system, assuming that the initial state of the system is x(0), yi(k) ∈ Rmy is the measured value of the ith sensor, ω(k)is the process noise, assuming it is Gaussian white noise, the mean is 0, and the variance is Q ≥ 0, vi(k) ∈ Rny is the measurement noise, assuming it is Gaussian white noise, the mean is 0, and the variance is Ri ≥ 0, A ∈ Rn×n, H ∈ Rm×n, x(k), ω(k), and vi(k) are mutually independent
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
