CN112487425B - Method for realizing consistency of multi-agent system under condition of spoofing attack - Google Patents
Method for realizing consistency of multi-agent system under condition of spoofing attack Download PDFInfo
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Abstract
The invention provides a method for realizing consistency of a multi-agent system under the condition of spoofing attack, and belongs to the technical field of networked multi-agent systems. The method for predicting and controlling the safe network can actively compensate the influence of network time lag, predict the state of a system in normal operation, ensure that the multi-agent system can still keep data in normal update when being attacked and achieve mean square consistency.
Description
Technical Field
The invention relates to a method for realizing consistency of a multi-agent system under the condition of spoofing attack, belonging to the technical field of networked multi-agent systems.
Background
In recent years, multi-agent systems have shown a number of unique advantages such as flexible architecture, installation and maintenance costs, and global integration and sharing of resources, and thus, multi-agent systems have been widely used in the fields of traffic management, robot control, mobile sensor networks, teleoperation, unmanned aerial vehicles, remote control, and the like.
However, since network sharing, particularly wired and wireless networks, has a strong openness, control data exchanged through the network in the multi-agent system faces network security problems without security protection. For example, industrial spy remotely obtains confidential information of critical devices. Malicious hackers intercept, tamper, forge and forward data transmitted by the network with sensitive information. In particular, for multi-agent network control systems of critical infrastructure such as water, electricity, nuclear and chemical plants, interruption of any one of the multi-agent network control systems can lead to serious consequences, from loss of production to environmental destruction, even personal injury or life loss. These network attacks against multi-agent systems are truly existing, and how to ensure that multi-agent systems achieve consistency in the case of spoofing attacks is an emerging issue.
Disclosure of Invention
The invention provides a method for processing spoofing attack, which combines a state estimation method with a threshold value method, and solves the problem of safety consistency of a multi-agent system when the multi-agent system is subject to spoofing attack.
The invention discloses a method for realizing consistency of a multi-agent system under the condition of spoofing attack, which adopts the following technical scheme:
Step one, establishing a discrete time dynamic model of a networked multi-agent system;
Modeling aiming at a spoofing attack model of a networked multi-agent system;
Constructing a state observer aiming at a discrete time dynamic model of the networked multi-agent system, and carrying out state prediction;
step four, designing a consistency control protocol according to the step three to obtain a compact expression form of a state error equation so as to achieve the consistency of states;
comparing the state obtained by prediction estimation with the state directly received by using a threshold comparison method, and finishing the safe update of the state to ensure the consistency of multiple intelligent agents under the spoofing attack;
the following steps are described:
The discrete time state equation of the multi-agent system in the step one is as follows:
xi(k+1)＝Aixi(k)+Biui(k)+wi(k)
yi(k)＝Cixi(k)+vi(k)
Wherein x i (k) is a discrete time dynamic model state vector of an ith agent at k moment in the networked multi-agent system, x i (k+1) is a discrete time dynamic model state vector of the ith agent at k+1 moment, y i (k) is a measurement output function u i (k) of the ith agent at k moment, w i(k)∈Rn is system noise, v i(k)∈Rn is measurement noise, and both A i,Bi and C i are system matrices.
Modeling aiming at a model of spoofing attack of the multi-agent system:
xi a(k)＝xi(k)+pij(k)aij(k)
a ij (k) is any bounded real number, spoofing attacks obey conditional probabilities, and design P ij (k) represents decision variables acting on agent i to agent j communication:
the state observer constructed in the step three and the state prediction thereof are specifically formed as follows:
the following form observer is designed:
wherein, Is the state prediction of the previous step, u i (k) is the observer input at time k, and G is the observer gain.
Because there is a time lag τ in the information of agent i obtaining agent j, to overcome the effect of the network time lag, the state of agent j from time k- τ to time k is constructed based on the data of agent j until i=1, 2.
……
Through calculation, the following steps are obtained:
and step four, designing a consistency control protocol to obtain a compact expression form of a state error equation, wherein the specific form is as follows:
The following coherence protocol was designed for agent i:
Wherein the K-state feedback gain matrix;
If and only if When the intelligent system is established, the multi-intelligent system can reach the consistent state;
the order state error can be expressed as:
δi(k)＝xi(k)-x1(k)，
thus, the state error system compact equation is:
Wherein the method comprises the steps of Is suler stable and the whole system is stable.
The safety mechanism designed by the threshold comparison method in the fifth step is specifically formed by:
The threshold mechanism is as follows:
In the presence of spoofing attacks, the state expression of the multi-agent system:
When communication between agent i and agent j at time k is fraudulently attacked, if The agent receives a batch status update of status x j a (k) over the network, otherwise, through status estimation/>And updating the batch state, and ensuring the consistency accuracy by adjusting the range of the error epsilon.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a multi-intelligent system communication topology in an example;
FIG. 3 is a graph of state components of an example multi-agent system when it is subject to a spoofing attack;
Fig. 4 is a graph of state components of an example multi-agent system employing a threshold mechanism in the event of a spoofing attack.
Detailed Description
The invention is further described below with reference to the drawings and the detailed description.
Establishing a discrete time dynamic model of the networked multi-agent system:
In connection with fig. 1, the flow chart of the present invention is as follows:
Establishing a discrete time dynamic model of the networked multi-agent system;
xi(k+1)＝Aixi(k)+Biui(k)+wi(k)
yi(k)＝Cixi(k)+vi(k)
Wherein x i (k) is a discrete time dynamic model state vector of an ith agent at k moment in the networked multi-agent system, x i (k+1) is a discrete time dynamic model state vector of the ith agent at k+1 moment, y i (k) is a measurement output function of the ith agent at k moment, u i (k) represents a consistency control protocol of the ith agent, w i(k)∈Rn is system noise, v i(k)∈Rn is measurement noise, and both A i,Bi and C i are system matrices.
Modeling against spoofing attacks:
xi a(k)＝xi(k)+pij(k)aij(k)
wherein P ij (k) represents a decision variable acting on agent i to communicate with agent j:
designing a state observer:
wherein, Is the state prediction of the previous step, u i (k) is the observer input at time k, and G is the observer gain.
Based on the state of agent j at time k- τ, predicting the state of agent j at time k:
The following coherence protocol was designed for agent i:
If and only if When the intelligent system is established, the multi-intelligent system can reach the consistent state.
Defining the state error manifestation as:
δi(k)＝xi(k)-x1(k),i＝1,2,...,N,
the compact equation for the state error system is:
Wherein the method comprises the steps of Is suler stable and the whole system is stable.
The threshold mechanism is designed as follows:
The threshold mechanism is as follows:
When communication between agent i and agent j at time k is fraudulently attacked, if The agent receives a batch status update of status x j a (k) over the network, otherwise, through status estimation/>And updating the batch state, and ensuring the consistency accuracy by adjusting the range of the error epsilon.
The results of the present invention were verified using the following examples:
A network comprising 4 agents with a communication topology as shown in figure 2.
The system parameters are as follows:
the adjacency matrix is:
The probability of a spoofing attack is p ij (k) =0.2, the intensity a ij (k) =1.5, and the error epsilon=0.005.
Fig. 3 is a graph of the state components of a multi-agent system when it is under a spoofing attack.
Fig. 4 is a graph of state components of a multi-agent system employing a threshold mechanism in the event of a spoofing attack.
The above description is only one embodiment of the present invention and is not intended to limit the present invention, and various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. The method for realizing consistency of the multi-agent system under the condition of spoofing attack is characterized by comprising the following steps:
Step one, establishing a discrete time dynamic model of a networked multi-agent system; the discrete time dynamic model specifically comprises the following steps:
xi(k+1)＝Axi(k)+Bui(k)+wi(k) (1)
yi(k)＝Cxi(k)+vi(k) (2)
Wherein x i (k) is a discrete time dynamic model state vector of an ith agent at k moment in a networked multi-agent system, x i (k+1) is a discrete time dynamic model state vector of the ith agent at k+1 moment, y i (k) is a measurement output function of the ith agent at k moment, u i (k) represents a consistency control protocol of the ith agent, w i(k)∈Rn is system noise, v i(k)∈Rn is measurement noise, and A, B and C are system matrixes;
Modeling aiming at a spoofing attack model of a networked multi-agent system; the attack model of the spoofing attack is as follows:
xi a(k)＝xi(k)+pij(k)aij(k) (3)
spoofing attacks obey conditional probabilities, let a ij (k) be any bounded real number, where P ij (k) represents a decision variable acting on agent i to communicate with agent j:
Constructing a state observer aiming at a discrete time dynamic model of the networked multi-agent system, and carrying out state prediction;
Step four, designing a consistency control protocol according to the step three to obtain a compact expression form of a state error equation so as to achieve the consistency of states;
Step five, a safety mechanism is designed by utilizing a threshold comparison method, and the state obtained by prediction estimation is compared with the state directly received, so that the safety updating of the state is completed, and the consistency of multiple intelligent agents under the spoofing attack is ensured; the safety mechanism is as follows:
The threshold mechanism is as follows:
In the presence of a spoofing attack, the state expression of the multi-agent system is:
When communication between agent i and agent j at time k is fraudulently attacked, if The agent receives a batch status update of status x j a (k) over the network, otherwise, through status estimation/>And updating the batch state, and ensuring the consistency accuracy by adjusting the range of the error epsilon.
2. The method for implementing consistency in a multi-agent system according to claim 1, wherein the state observer and the state prediction in the step three are as follows:
the following form observer is designed:
wherein, Is the state prediction of the previous step, u i (k) is the observer input at time k, G is the observer gain;
Because there is a time lag τ in the information of agent j obtained by agent i, to overcome the effect of the network time lag, the state of agent j from time k- τ to time k is constructed based on the data of agent j until time i=1, 2.
……
Through calculation, the following steps are obtained:
3. the method for implementing consistency in a multi-agent system according to claim 2, wherein the compact representation of the consistency control protocol and the state error equation in step four is:
The following coherence protocol was designed for agent i:
wherein K represents the state feedback gain matrix;
If and only if When the intelligent agent system is established, the multi-intelligent agent system achieves the consistent state;
the order state error can be expressed as:
Thus, the compact equation for the state error system is:
Wherein the method comprises the steps of Is suler stable and the whole system is stable.
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| CN113110113B (en) * | 2021-05-14 | 2023-10-24 | 哈尔滨理工大学 | Method for realizing grouping consistency of discrete multi-agent system with communication constraint |
| CN114935931B (en) * | 2022-05-07 | 2023-09-15 | 苏州科技大学 | Time-varying heterogeneous multi-agent consistency control method and system |
| CN115022031B (en) * | 2022-05-31 | 2023-07-21 | 重庆邮电大学 | Safety consistency control method for solving influence of FDI attack on multi-agent system |
| CN115718427B (en) * | 2022-11-16 | 2023-06-13 | 哈尔滨理工大学 | Non-fragile network prediction control method for security |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010041788A1 (en) * | 2008-10-10 | 2010-04-15 | Plustech Inc. | A method for neutralizing the arp spoofing attack by using counterfeit mac addresses |
| CN105204035A (en) * | 2015-10-10 | 2015-12-30 | 清华大学 | Satellite navigation relay-style spoofing attack defending method and device |
| CN108933772A (en) * | 2018-03-19 | 2018-12-04 | 和芯星通（上海）科技有限公司 | Attack detection method and device, computer readable storage medium and terminal |
| CN110376889A (en) * | 2019-07-12 | 2019-10-25 | 哈尔滨理工大学 | Heterogeneous network multi-agent system with Time-varying time-delays is grouped consistent method |
-
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- 2020-11-23 CN CN202011318573.0A patent/CN112487425B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010041788A1 (en) * | 2008-10-10 | 2010-04-15 | Plustech Inc. | A method for neutralizing the arp spoofing attack by using counterfeit mac addresses |
| CN105204035A (en) * | 2015-10-10 | 2015-12-30 | 清华大学 | Satellite navigation relay-style spoofing attack defending method and device |
| CN108933772A (en) * | 2018-03-19 | 2018-12-04 | 和芯星通（上海）科技有限公司 | Attack detection method and device, computer readable storage medium and terminal |
| CN110376889A (en) * | 2019-07-12 | 2019-10-25 | 哈尔滨理工大学 | Heterogeneous network multi-agent system with Time-varying time-delays is grouped consistent method |
Non-Patent Citations (1)
| Title |
|---|
| 基于事件触发和欺骗攻击的多智能体一致性控制;王誉达 等;南京信息工程大学学报;第第11卷卷(第第4期期);380- 389 * |
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