A decentralized preset performance control method and system for resisting spoofing attacks

Abstract

The application relates to the technical field of nonlinear interconnection system control methods, and provides a decentralized preset performance control method and system for resisting deception attacks, which comprises the following steps: a high-order nonlinear pure feedback interconnection system model is established; the model contains unmodeled dynamics, and the unmodeled dynamics satisfy the joint bounded input-bounded state property; a preset performance function is designed for each state variable; based on the preset performance function, a local controller and a virtual control law are constructed for each subsystem through a recursive method; when the system is subjected to a deception attack acting on a sensor-controller channel or a controller-actuator channel, a local controller is designed based on the state variable subjected to the deception attack through the same recursive method, and the preset performance function is adjusted to compensate for the influence of the attack on the convergence rate, so that the state variable still converges to zero at a specified convergence rate. The application solves the problems of comprehensiveness, anti-uncertainty and robustness against deception attacks in preset performance regulation of current methods.

Description

Technical Field

[0001] This invention relates to the field of control methods for nonlinear interconnected systems, and in particular to a distributed preset performance control method and system for resisting deception attacks. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] As industrial systems become increasingly intelligent, nonlinear interconnected systems are finding wider applications in fields such as smart grids, autonomous driving, and distributed robotics. These systems consist of multiple interconnected subsystems that achieve collaborative functions through the exchange of matter, energy, or information. However, their inherent nonlinear characteristics, inter-subsystem coupling, and unmodeled dynamics place stringent demands on the stability, robustness, and dynamic performance of control methods.

[0004] Preset performance control constrains tracking error, convergence speed, and overshoot within specified ranges by designing performance functions, thus achieving proactive dynamic performance regulation. However, existing preset performance control is mostly designed for centralized systems, requiring global state information, which leads to heavy communication burden, poor real-time performance, and low fault tolerance. While distributed control can alleviate these problems, existing distributed preset performance control schemes still have significant limitations: First, the convergence rate adjustment lacks flexibility, making it difficult to accurately preset the convergence rate and ensuring the boundedness of the weighted state across the entire time domain; second, transient performance constraints are singular, with weak comprehensive constraint capabilities on multiple dimensions such as final accuracy, arrival time, and overshoot; and third, anti-interference capabilities are weak, with uncertainties in the system and external disturbances easily leading to a decline in control performance or even instability.

[0005] Furthermore, nonlinear interconnected systems face severe cybersecurity threats. Spoofing attacks inject false information by tampering with sensor or controller data, disrupting system stability. Existing anti-attack methods are mostly focused on centralized architectures or linear systems, lacking decentralized solutions suitable for high-order nonlinear pure feedback interconnected systems. They struggle to handle the time-varying uncertainties of attacks and fail to be designed in conjunction with preset performance indicators. When a system simultaneously faces unmodeled dynamics and spoofing attacks, existing methods struggle to balance robustness and preset performance, easily leading to problems such as decreased control accuracy and slower convergence speed. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides a distributed preset performance control method and system for resisting deception attacks. It designs a distributed preset performance control method that combines accurate preset convergence rate, multi-dimensional transient performance constraints, and strong anti-interference and anti-deception attack capabilities. This solves the problem that current distributed control methods for nonlinear interconnected systems have significant shortcomings in terms of the comprehensiveness of preset performance regulation, resistance to uncertainty, and robustness against deception attacks.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a distributed preset performance control method for resisting spoofing attacks, comprising: A high-order nonlinear pure feedback interconnected system model is established; the system consists of N subsystems, each containing n state variables; the model includes unmodeled dynamics, and the unmodeled dynamics satisfy the joint bounded input-bounded state property; A preset performance function is designed for each state variable. The preset performance function belongs to a preset function set and is used to pre-specify the convergence rate of the state variable. Based on the preset performance function, a local controller and virtual control law are constructed for each subsystem using a recursive approach. When the system is subjected to a spoofing attack on the sensor-controller channel or the controller-actuator channel, the local controller is designed using the same recursive method based on the state variables of the spoofed attack. The preset performance function is adjusted to compensate for the impact of the attack on the convergence rate, so that the state variables still converge to zero at the specified convergence rate.

[0008] The high-order nonlinear pure feedback interconnected system model is represented as follows:

[0009] in, Indicates unmodeled dynamics, and Not suitable for feedback; , , For the first The first subsystem One state variable; To control the input, For system output; It is an unknown continuously differentiable function.

[0010] As a further technical solution, the preset function set is defined as follows:

[0011] in, Indicates from arrive Locally absolutely continuous functions A set; It is a constant.

[0012] As a further technical solution, the recursive method is expressed as follows:

[0013] in, For error variables, For virtual controllers, For time-varying gain, Pick , , Design parameters that are positive constants. Functions of type Nussbaum For record It is about and The function.

[0014] As a further technical solution, the deception attack model is as follows:

[0015] in, and These represent the system state and control inputs after an attack; and It is an unknown, time-varying, and bounded function; It is an unknown continuously differentiable function, and satisfies ; Assumption and And record .

[0016] As a further technical solution, the method for adjusting the preset performance function is as follows: when each... When the lower bound is reached, the local controller will be... Adjusted to This ensures that the original state is maintained. The rate converges to zero; simultaneously satisfying

[0017] in, for The lower bound.

[0018] As a further technical solution, the control system output tracks the target at a preset rate, so that the output tracking error converges to zero at a preset rate.

[0019] A second aspect of the present invention provides a distributed preset performance control system for resisting spoofing attacks, comprising: The model building module is configured to: establish a high-order nonlinear pure feedback interconnected system model; the system consists of N subsystems, each subsystem containing n state variables; the model includes unmodeled dynamics, and the unmodeled dynamics satisfy the joint bounded input-bounded state property; The standard setting module is configured to: design a preset performance function for each state variable, wherein the preset performance function belongs to a preset function set and is used to pre-specify the convergence rate of the state variable; The standard execution module is configured to: construct a local controller and a virtual control law for each subsystem based on the preset performance function using a recursive approach; The deception attack handling module is configured to: when the system suffers a deception attack on the sensor-controller channel or the controller-actuator channel, design a local controller based on the state variable of the deception attack using the same recursive method, and compensate for the impact of the attack on the convergence rate by adjusting the preset performance function, so that the state variable still converges to zero at the specified convergence rate.

[0020] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a distributed preset performance control method for resisting spoofing attacks as described in the first aspect of the present invention.

[0021] A fourth aspect of the present invention provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of a distributed preset performance control method for resisting spoofing attacks as described in the first aspect of the present invention.

[0022] The above one or more technical solutions have the following beneficial effects: This invention proposes a novel decentralized control scheme by leveraging robust local adaptive gain: a pre-defined performance function is designed for each state variable, and a performance function is designed for each tracking error. For nonlinear interconnected systems containing unmodeled dynamics, it is assumed that the unmodeled dynamics satisfy the Jointly Bounded Input-Bounded State (BIBS) property, thus establishing a unified attack model to resist spoofing attacks targeting the sensor-controller channel and the controller-actuator channel. This scheme not only effectively mitigates structural uncertainties but also constrains the key signals of each subsystem within a pre-defined performance funnel. Therefore, global stabilization with a pre-defined convergence rate and global practical tracking with pre-defined transient performance are achieved, and rigorous performance analysis is performed in a decentralized manner.

[0023] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0024] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0025] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the state variables that realize the control target (I) in an embodiment of the present invention; Figure 3 This is how the control objective (I) is achieved in the embodiments of the present invention. A schematic diagram of the curve; Figure 4 This is a schematic diagram of the curves for achieving the control target (I) control input in an embodiment of the present invention; Figure 5 This is a schematic diagram of the tracking trajectory of the control target (II) in an embodiment of the present invention; Figure 6 This is a schematic diagram of the state variables that achieve the control objective (II) in an embodiment of the present invention; Figure 7 This is a schematic diagram of the control input curve for achieving control target (II) in an embodiment of the present invention; Figure 8 This is a schematic diagram of the state variable curves when resisting deception attacks in an embodiment of the present invention; Figure 9 In this embodiment of the invention, the state variable is used to resist deception attacks. The rate convergence intention. Detailed Implementation

[0026] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0027] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0028] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0029] With the popularization of Industrial Internet and Internet of Things (IoT) technologies, nonlinear interconnected systems often rely on networks for communication and control command transmission between subsystems. This exposes these systems to severe cybersecurity threats, with spoofing attacks being the most typical and damaging. Spoofing attacks inject false information into the system by tampering with sensor-collected status data and controller-sent control commands, causing the controller to make decisions based on erroneous data and compromising system stability and control performance. Existing anti-spoofing attack control methods mainly focus on centralized architectures or are only applicable to specific types of linear systems, lacking distributed solutions suitable for high-order nonlinear pure feedback interconnected systems. These methods struggle to handle the time-varying uncertainties introduced by attacks. Deceptive attacks, such as tampering with gains and injecting interference, often involve unknown and time-varying parameters, making it difficult for existing methods to accurately model and adaptively compensate for them. Furthermore, they lack co-design with preset performance metrics. Most anti-attack methods focus solely on ensuring system stability, failing to integrate anti-attack capabilities with preset convergence rates, transient performance, and other metrics, thus failing to meet the quantitative requirements for control performance in complex scenarios. In addition, they lack robustness to unmodeled dynamics. When both unmodeled dynamics and deceptive attacks exist simultaneously, existing methods struggle to balance robustness and preset performance, easily leading to decreased control accuracy and slower convergence speeds.

[0030] Therefore, current distributed control methods for nonlinear interconnected systems have significant shortcomings in terms of the comprehensiveness of preset performance regulation and robustness against uncertainty and deception attacks, and cannot meet the requirements of modern engineering systems for high-precision, high-reliability, and high-security control.

[0031] This invention proposes a distributed preset performance control method that combines precise preset convergence rate, multi-dimensional transient performance constraints, and strong anti-interference and anti-spoofing attack capabilities. It solves the core problems of insufficient performance regulation, weak robustness, and lack of anti-attack capability in the existing technology, and provides technical support for the safe and stable operation of complex nonlinear interconnected systems.

[0032] Example 1 like Figure 1 As shown, this embodiment discloses a distributed preset performance control method for resisting spoofing attacks, including: S1: Establish a high-order nonlinear pure feedback interconnected system model; the system consists of N subsystems, each subsystem containing n state variables; the model includes unmodeled dynamics, and the unmodeled dynamics satisfy the joint bounded input-bounded state property.

[0033] S2: Design a preset performance function for each state variable. The preset performance function belongs to a preset function set and is used to pre-specify the convergence rate of the state variable.

[0034] S3: Based on the preset performance function, construct a local controller and virtual control law for each subsystem in a recursive manner.

[0035] S4: When the system is subjected to a spoofing attack on the sensor-controller channel or the controller-actuator channel, the local controller is designed using the same recursive method based on the state variables of the spoofed attack, and the convergence rate of the state variables is maintained by adjusting the preset performance function.

[0036] Specifically, in step S1, a high-order nonlinear pure feedback interconnection system model is established.

[0037] Consider a nonlinear interconnected system consisting of N subsystems, each with an n-dimensional state. The model of the high-order nonlinear pure feedback interconnected system is shown in Equation (1):

[0038] in, Indicates unmodeled dynamics, and Not for feedback. , , For the first The first subsystem There are several state variables.

[0039] In this embodiment, Regarding input It satisfies the Joint Bounded Input-Bounded State (BIBS) property. That is, for any initial conditions, as long as

[0040] Then there must be

[0041] in, .

[0042] In equation (1), and The first The control inputs and system outputs of each subsystem.

[0043] They represent the first The state vectors of each subsystem; as well as All are unknown, continuously differentiable functions. For convenience, this embodiment will use the term... Partial derivatives The sign remains unchanged but is unknown, and there exists an unknown positive continuous function. Make

[0044] in .

[0045] Specifically, in step S2, for each state variable Design a pre-defined function. , to make the state With 1 / The rate converges to zero. , And assume that there exists a positive constant. Makes any All satisfy:

[0046] Among them, set Defined as:

[0047] in Indicates from arrive Locally absolutely continuous functions A set; It is a constant.

[0048] Specifically, in step S3, a preset performance control law is designed. With virtual control law .

[0049] Regarding the first For each subsystem, design the following local controller:

[0050] in, denoted as u is about and The function.

[0051] The control law is constructed recursively as follows:

[0052] in, For error variables; For virtual controllers, For time-varying gain, the intermediate variables in the controller recursive design; We can take 1 / (1-s). For record It is about and The function. Initial virtual control quantity. The remaining design parameters and functions should be reasonably selected based on the subsequent control objectives, and should satisfy the following conditions: Design parameters that are positive constants; It is a strictly monotonically increasing and unbounded function, and is a time-varying gain. It can handle uncertain nonlinear terms in the system and satisfies the following derivative growth condition: ,in , Indicates from arrive The set of continuously differentiable functions. Functions Satisfies the properties of Nussbaum type functions:

[0053] Pick For a given function, the controller constructed by equations (2) and (3) ensures that: For any initial conditions:

[0054] The resulting closed-loop system in the interval There exists a unique solution. and satisfy

[0055]

[0056] in , .

[0057] In other words, each state variable All at rate It converges to zero.

[0058] In other words, the goals to be achieved are as follows: Objective (I): Globally stable control with a preset convergence rate: for each state variable in the system All at a pre-specified rate Converging to zero ( (for a given preset function), satisfying This means that the weighted states remain bounded throughout the entire time interval, thus guaranteeing the required convergence rate. Simultaneously, all unmodeled dynamics remain bounded throughout the entire time domain.

[0059] In this embodiment, take Given a function, These are parameters related to the final convergence accuracy of the tracking error, where

[0060] The controller constructed by equations (2) and (3) guarantees: For any satisfying initial conditions The unique solution of the obtained closed-loop system lies in the interval It remains bounded and satisfies .

[0061] In other words, the goals to be achieved are as follows: Objective (II): Let For the first The reference signal of each subsystem corresponds to the tracking error. The following constraints must be satisfied:

[0062]

[0063] At the same time, all state variables of the system remain bounded throughout the entire time interval. In particular, Given a pre-defined function, and through proper design, ensure that each tracking error is simultaneously controlled. The preset final accuracy, preset arrival time, and preset maximum overshoot.

[0064] This embodiment considers the possibility of network spoofing attacks during system operation and constructs a flexible control scheme to counter such attacks. Specifically, assuming that the sensor-to-controller channel, the controller-to-actuator channel, and each subsystem are potentially vulnerable to spoofing attacks, the spoofing behavior of system state variables and control inputs can be described as follows: (4) in, and These represent the system state and control inputs after an attack. and It is an unknown, time-varying, and bounded function, while It is an unknown continuously differentiable function, and satisfies Assuming and And record .

[0065]

[0066] Based on the original system (1), the nonlinear interconnected system under the deception attack can be rewritten in the following form: (5) in

[0067]

[0068]

[0069] In fact, if

[0070] Combination and The mapping relationship can guarantee

[0071] therefore

[0072] On the other hand, system (4) satisfies

[0073] in,

[0074] and

[0075] For the attacked system (5) The subsystem is designed with the following local controller:

[0076] This controller is generated through the following recursive process:

[0077] Consider a system (5) subjected to a deception attack (4), and let the given function be...

[0078] The controller constructed using equation (6) can guarantee that: For any initial conditions

[0079] Closed-loop system There exists a unique solution. And satisfy

[0080] and

[0081] In particular, ,in for The lower bound, that is by The rate converges to zero.

[0082] If each is known The lower bound of can be obtained by appropriately adjusting the function in equation (6) to make It converges to zero at a given rate.

[0083] For example, if you want each by The rate convergence can be achieved in the controller. Adjusted to .

[0084] In other words, the goals to be achieved are as follows: Target (III): For each attacked system, will be The rate converges to zero. (Combined) Boundedness and From the definition, we can obtain Furthermore, if lower bound Given that, then by and It can be deduced that That is, each by The rate converges to zero.

[0085] In this embodiment, to verify the effectiveness of the above method, an interconnected system consisting of two inverted pendulums coupled by springs is considered, whose dynamic equation is:

[0086] in The first The pendulum's swing angle, endpoint mass, and control input. The pendulum length is set. The system parameters are selected as follows: , , , , The initial state is set as follows: , , , .

[0087] make , For target (I) (global stabilization with a specified convergence rate), select , , And the control gain vector is set to .

[0088] For target (II) (output tracking with specified transient performance), the same method as for target (I) is used. and Simultaneously set a reference tracking signal Performance function , The control gain vector is set to .

[0089] Figures 2-4 , Figures 5-7 The trajectories of the system signals under targets (I) and (II) are shown respectively. For example, Figure 2 and Figure 3 As shown, system status Faster The rate converges to the origin. For example... Figure 6 The figure shows the tracking error. and The trajectory shows that the tracking error reached the final specified accuracy of 0.015 within the specified time of 10 seconds, and the maximum overshoot was less than 10%.

[0090] Verification against spoofing attacks: Assume that the sensor-controller channel and controller-actuator channel of system (7) are both subjected to the following spoofing attacks: Sensor-controller channel attack:

[0091]

[0092] Controller-actuator channel attack:

[0093] Design a controller in the form of (5); in, , , , The control gain vector is set to Select system parameters and initial state. , , , The simulation results are shown in Figure 8, which shows the original state of the system. It can still converge to the origin. If it is known... The lower realm will , replace , , Replace with Then the state Able to be faster than The rate converges to zero, as shown in Figure 9.

[0094] Example 2 This embodiment discloses a distributed preset performance control system for resisting spoofing attacks, including: The model building module is configured to: establish a high-order nonlinear pure feedback interconnected system model; the system consists of N subsystems, each subsystem containing n state variables; the model includes unmodeled dynamics, and the unmodeled dynamics satisfy the joint bounded input-bounded state property; The standard setting module is configured to: design a preset performance function for each state variable, wherein the preset performance function belongs to a preset function set and is used to pre-specify the convergence rate of the state variable; The standard execution module is configured to: construct a local controller and a virtual control law for each subsystem based on the preset performance function using a recursive approach; The deception attack handling module is configured to: when the system suffers a deception attack on the sensor-controller channel or the controller-actuator channel, design a local controller based on the state variable of the deception attack using the same recursive method, and compensate for the impact of the attack on the convergence rate by adjusting the preset performance function, so that the state variable still converges to zero at the specified convergence rate.

[0095] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0096] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of a distributed preset performance control method for resisting spoofing attacks as described in Embodiment 1 of this disclosure.

[0097] Example 4 The purpose of this embodiment is to provide an electronic device.

[0098] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a distributed preset performance control method for resisting spoofing attacks as described in Embodiment 1 of this disclosure.

[0099] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0100] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0101] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A distributed preset performance control method for resisting spoofing attacks, characterized in that, include: Establish a model of a high-order nonlinear pure feedback interconnected system; The system consists of N subsystems, and each subsystem contains n state variables; The model contains unmodeled dynamics, and the unmodeled dynamics satisfy the joint bounded input-bounded state property; A preset performance function is designed for each state variable. The preset performance function belongs to a preset function set and is used to pre-specify the convergence rate of the state variable. Based on the preset performance function, a local controller and virtual control law are constructed for each subsystem using a recursive approach. When the system is subjected to a spoofing attack on the sensor-controller channel or the controller-actuator channel, the local controller is designed using the same recursive method based on the state variables of the spoofed attack. The preset performance function is adjusted to compensate for the impact of the attack on the convergence rate, so that the state variables still converge to zero at the specified convergence rate.

2. The distributed preset performance control method for resisting deception attacks as described in claim 1, characterized in that, The high-order nonlinear pure feedback interconnected system model is represented as follows: in, Indicates unmodeled dynamics, and Not suitable for feedback; , , For the first The first subsystem One state variable; To control the input, For system output; It is an unknown continuously differentiable function.

3. The distributed preset performance control method for resisting deception attacks as described in claim 1, characterized in that, The preset function set is defined as follows: in, Indicates from arrive Locally absolutely continuous functions A set; It is a constant.

4. The distributed preset performance control method for resisting spoofing attacks as described in claim 1, characterized in that, The recursive method is expressed as follows: in, For error variables, For virtual controllers, For time-varying gain, Pick , , Design parameters that are positive constants. Functions of type Nussbaum For record It is about and The function.

5. A distributed preset performance control method for resisting spoofing attacks as described in claim 1, characterized in that, The deception attack is modeled as follows: in, and These represent the system state and control inputs after an attack; and It is an unknown, time-varying, and bounded function; It is an unknown continuously differentiable function, and satisfies ; Assumption and And record .

6. A distributed preset performance control method for resisting spoofing attacks as described in claim 5, characterized in that, The method for adjusting the preset performance function is as follows: when each... When the lower bound is reached, the local controller will be... Adjusted to This ensures that the original state is maintained. The rate converges to zero; simultaneously satisfying in, for The lower bound.

7. A distributed preset performance control method for resisting spoofing attacks as described in claim 1, characterized in that, The control system output tracks the target at a preset rate, even if the output tracking error converges to zero at the preset rate.

8. A distributed preset performance control system for resisting spoofing attacks, characterized in that: include: The model building module is configured to: build a high-order nonlinear pure feedback interconnected system model; The system consists of N subsystems, each containing n state variables; the model includes unmodeled dynamics, and the unmodeled dynamics satisfy the joint bounded input-bounded state property. The standard setting module is configured to: design a preset performance function for each state variable, wherein the preset performance function belongs to a preset function set and is used to pre-specify the convergence rate of the state variable; The standard execution module is configured to: construct a local controller and a virtual control law for each subsystem based on the preset performance function using a recursive approach; The deception attack handling module is configured to: when the system suffers a deception attack on the sensor-controller channel or the controller-actuator channel, design a local controller based on the state variable of the deception attack using the same recursive method, and compensate for the impact of the attack on the convergence rate by adjusting the preset performance function, so that the state variable still converges to zero at the specified convergence rate.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of a distributed preset performance control method for resisting spoofing attacks as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the distributed preset performance control method for resisting spoofing attacks as described in any one of claims 1-7.

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