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Optimal false data injection attack method for network self-triggering model predictive control

A model predictive control and false data technology, applied in the transmission system, electrical components, etc., can solve the problems of increasing the risk of detection and unrealistic control of samples, so as to reduce the probability of detection, optimize the overall performance quality, and improve efficiency. Effect

Active Publication Date: 2020-10-16
XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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Problems solved by technology

However, blindly selecting nodes for random attacks will definitely affect the attack effect, and for the expected attack effect, the attacker will be forced to increase the number of nodes to attack, thereby using more computing resources and increasing the risk of being detected by the system
Furthermore, from a defender's perspective, it may be impractical to protect all control samples given that defense resources are very limited

Method used

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  • Optimal false data injection attack method for network self-triggering model predictive control
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  • Optimal false data injection attack method for network self-triggering model predictive control

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Embodiment 1

[0066] refer to Figure 4 , the state of the wheeled intelligent robot system is expressed as χ=[xyθ] T (should be [x, y, θ]), which consists of the vehicle's position [xy] should be [x, y] and direction θ, u = [v, ω] T (less comma) is the control input, and the constraints are and Calculated Lipschitz constant and the normal constant L G respectively and L G = 1.0, the stage and terminal cost functions are given by F = χ T Qχ+u T Ru, V f =χ T χ is given, where Q=0.1I 3 , R=0.05I 2 .

[0067]

[0068] Apply the optimal false data injection attack to the wheeled intelligent robot system based on network self-triggering model predictive control, select the number of samples as N=5, and the number of attack points in each data packet as M=2. The specific process is as follows:

[0069] 1) Obtain and capture self-trigger control data and sampling interval;

[0070] 2) Calculate the upper bound of the system state error before and after the false data injection...

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Abstract

The invention discloses an optimal false data injection attack method for network self-triggering model predictive control. The method comprises the following steps: 1) collecting control data and sampling interval data in a network transmission packet; 2) calculating all system state error upper bound values before and after attacks under different attack times; and 3) taking the point corresponding to the maximum error upper bound value before and after the attack as the optimal false data injection point to perform the attack, and completing the optimal false data injection attack for the prediction control of the network self-triggering model, so that the optimal false data injection attack can be quickly established, the attack efficiency is improved, and the probability of being detected by the system is reduced.

Description

technical field [0001] The invention relates to an optimal false data injection attack method, in particular to an optimal false data injection attack method oriented to network self-triggering model predictive control. Background technique [0002] With the construction of 5G base stations in the country, network control and driverless technology are becoming a technology with broad application prospects. Just like the large-scale use of industrial robots, it is of great value whether it is liberating human labor or replacing people in high-risk environments. However, with the development of artificial intelligence technology, network control gradually shows a more open trend, and is no longer limited to the closed network environment in the past. At the same time, due to the application of a large number of new technologies in network control, in an open and interoperable network state, network security issues have attracted more and more attention. When the control netwo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/06
CPCH04L63/1466H04L63/1433
Inventor 贺宁马凯沈超
Owner XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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