A method for controlling consistency of secure operation of a communication network

Abstract

The application discloses a kind of consistency control methods of safe operation of communication network, the method includes the following steps: S1, the dynamic change in the process of communication network operation is analyzed, communication network node is regarded as point set, the communication state of data interaction between each communication network node is regarded as edge set and is constructed communication relation diagram, and multi-agent system model is established;S2, the random packet loss framework in the operation of communication network is constructed;S3, the consistency control protocol in the operation of communication network under the condition of fraud attack is designed;S4, for the model, framework and designed control protocol established, the system is positively constrained and verified;S5, the consistency of communication network system under random packet loss and fraud attack is verified.The method of the application models communication network using positive system, designs distributed consistency control protocol, solves the data packet loss problem in network communication process, and ensures the consistency control of safe operation of communication network system data.

Description

Technical Field

[0001] This invention belongs to the field of communication network technology, and specifically relates to a consistency control method for secure operation of communication networks. Background Technology

[0002] With the continuous development of internet technology, the internet has become an indispensable tool in people's daily lives, such as for office work, information retrieval, and news viewing. These uses generate a large amount of network data, and various problems arise during network transmission, such as packet loss and network attacks. These problems can affect work efficiency and may even lead to information security issues. Therefore, maintaining the normal operation of the network and ensuring the secure operation of the communication network system even after packet loss and network attacks are crucial.

[0003] A data communication network may consist of multiple subnets, with switching processes between them that may be random, perhaps relying on a semi-Markov random process. Each subnet contains multiple communication nodes with some kind of association between them; these nodes are considered corresponding agents. Many agents are connected to form a communication network system according to a certain communication method. For multi-agent systems, consensus control is an important aspect. Furthermore, the communication data in a network system is non-negative. If the state of a system is non-negative at any given time, then such a system is called a positive system. However, existing communication network models use general variables (which are both non-positive and non-negative), leading to redundancy in the system model and neglecting the non-negativity of data transmission volume. Moreover, they all use general system methods to analyze the data transmission process, which are computationally complex and computationally intensive. Therefore, adopting positive system analysis methods and implementing corresponding consensus control methods can more effectively analyze the changes in data packets during network data transmission. Summary of the Invention

[0004] The purpose of this invention is to provide a consistency control method for the secure operation of communication networks, in order to solve the above-mentioned problems.

[0005] According to an embodiment of the present invention, a consistency control method for secure operation of a communication network may include the following steps:

[0006] S1. Analyze the dynamic changes during the operation of the communication network, construct a communication relationship graph by taking the communication network nodes as a set of points and the communication states of data interaction between the communication network nodes as an edge set, and establish a multi-agent system model.

[0007] S2. Construct a framework for random packet loss during the operation of a communication network;

[0008] S3. Design a consistency control protocol for communication networks in the event of a deception attack.

[0009] S4. Verify the positive constraints of the system based on the established model, framework, and designed control protocol.

[0010] S5. Verify the consistency of the communication network system under random packet loss and spoofing attacks.

[0011] The present invention, by adopting the above technical solution, has the following beneficial effects:

[0012] 1. To address the issues of data packet loss and data transmission security in communication networks, this invention proposes a consistency control method for secure operation of communication networks. Each communication node is treated as an independent intelligent agent, and information transmission between subnets and between intelligent agents within a subnet is described using knowledge of multi-agent systems and graph theory.

[0013] 2. This invention uses positive systems to model the communication network, avoiding system model redundancy. Then, a distributed consistency control protocol is designed to solve the data packet loss problem during network communication, enabling data to be transmitted safely and stably, thereby making full use of the communication network system resources. Attached Figure Description

[0014] Figure 1 This is a schematic diagram showing the relationship between the various components in the communication network system of this invention;

[0015] Figure 2 This is a block diagram of the consistency control structure for secure operation of communication networks in this invention;

[0016] Figure 3 This is a flowchart of the consistency control method for secure operation of communication networks according to the present invention. Detailed Implementation

[0017] To further illustrate the various embodiments, the present invention provides accompanying drawings. These drawings are part of the disclosure of the present invention, primarily used to illustrate the embodiments and to explain the operating principles of the embodiments in conjunction with the relevant descriptions in the specification. With reference to these drawings, those skilled in the art should be able to understand other possible implementations and the advantages of the present invention. Components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0018] Figure 1 A communication network system framework is shown, which mainly includes a control center, a communication network, subnets, and communication terminals. Figure 2A consensus control structure for a multi-agent system is shown. This system is based on a semi-Markov transition process, where agents and the leader exchange information through a communication topology. Furthermore, a controller that considers the security of the transmission channel is designed.

[0019] like Figures 1-3 As shown in the figure, this embodiment provides a consistency control method for the secure operation of a communication network, and its specific steps are as follows:

[0020] S1. Verify the communication network system during random packet loss operation by collecting real-time data on the data transmission volume of the communication network nodes and establishing a state-space model of the communication network system. The specific process is as follows:

[0021] S1.1 Treat each communication network node as an independent agent, and establish the communication network operation process as a multi-agent system. Use graph theory to obtain the communication topology graph of the multi-agent system, and obtain its adjacency matrix and Laplace matrix, as follows:

[0022] To represent the correlation of communication between nodes in a communication network, a graph is introduced. in, For N communication network nodes, For edge set, Let a be an adjacency matrix. ij Let a represent the communication state of nodes i and j in the communication network. If communication is possible, then a ij =1, otherwise a ij =0, let the set of neighboring nodes of node i in the communication network be .

[0023] According to the topology diagram Laplace matrix and The information exchange matrix for consistency control is defined as follows: in, Let d be a diagonal matrix representing the constraint gain of the leader's communication network nodes on the follower's communication network nodes. If communication network node i can receive information from the leader's communication network node, then d i =1, otherwise d i =0.

[0024] S1.2. Collect data based on the state changes of communication network nodes and establish a state-space model of communication data in the communication network system, in the following form:

[0025]

[0026] in, Let represent the amount of data transmitted by node i in the communication network at time t, and let n represent the number of channels. It is the number of data packets sent from the control center by the i-th communication network node at time t; and Let these represent the known system matrix and input matrix under the σ(t) mode, respectively, obtained from sensor data collected during the actual data packet transmission process at time t; for convenience, let That is, A σ(t) =A p and B σ(t) =B p The function {σ(t), t≥0} represents a piecewise constant function dependent on time t, which is a semi-Markov transition signal that randomly switches between multiple subnets in a communication network system. Its update process follows the following probability transition:

[0027]

[0028] Where h>0 is the residence time of the transition rate; for all p,q∈S, we have This holds true, where Δt is the time increment, and ο(Δt) is a higher-order term of Δt; π pq (h)≥0 indicates the transition rate from subnet p to q, and and π pq The mathematical expectation of (h) is

[0029] S1.3. Based on the operational characteristics of the communication network, one of the communication network nodes is defined as the leader agent, and its dynamic equation is as follows:

[0030]

[0031] in, The leader agent represents the state of the agent, with the initial condition being x0(0) = x. 00 This can be considered a common strategy. Clearly, the amount of data transmitted between nodes in a communication network system has a non-negative characteristic.

[0032] S1.4 Based on the above steps, communication network node i and the leader communication network node satisfy the following consistency conditions:

[0033]

[0034] S2. Construct a framework for random packet loss during communication network operation. The random packet loss framework is constructed as follows:

[0035]

[0036] in, and Let ξ represent the actual amount of data transmitted from node i to node j and the actual amount of data transmitted from node i to leader node 0 at time t, respectively; random variable ξ ij (t) and ξ i0 (t) represent data packet loss in the follower-follower and leader-follower communication network nodes, respectively. In this application, it is assumed that ξ ij (t)=ξ(t) and ξ i0 (t)=ξ0(t), and have in, and All are expected values ​​and satisfy

[0037] S3. Design a consistency control protocol for a communication network under spoofing attacks. The specific process is as follows:

[0038] S3.1 The design form of the consistency control protocol for a communication network operating under a spoofing attack is as follows:

[0039]

[0040] Among them, f i (δ i (t) represents the deception attack variable; γ(t) represents a Bernoulli random variable independent of ξ(t) and ξ0(t), satisfying

[0041] K s(t) The controller gain that needs to be designed is, in the following form:

[0042]

[0043] Where ι=1,2,…,m, v p For an n-dimensional vector, Let T be an n-dimensional vector, and T be the transpose symbol.

[0044] S3.2. For the nonlinear attack signal in step S3.1, establish the following sector conditions:

[0045]

[0046] Where θ1 and θ2 are given positive constants and satisfy 0 < θ1 < θ2, and the initial value satisfies f i (0) = 0.

[0047] S3.3. Based on the communication topology, introduce the error variable: δ i (t)=x i(t)-x0(t). Furthermore, combining the system model and distributed protocol established in steps S1 and S3.1, the global error variable can be obtained, which has the following form:

[0048]

[0049] Among them, symbols Represents the Kronecker product, 1 n Represents a column vector with n elements all equal to 1; δ(t) is δ i The compact form of (t), namely Let f(δ(t)) be the time derivative of the compact form of the communication data error variable between the follower and leader agents at time t; f(δ(t)) is the deception attack variable f at time t. i (δ i The compact form of f(δ(t)) is f(δ(t)) = [f1 T (δ1(t)) f2 T (δ2(t)) … f N T (δ N (t))] T .

[0050] S3.4 The design conditions for secure data transmission in a communication network system under deception attacks are as follows:

[0051] Given constants θ1 > 0, θ2 > 0, and c > 0, if an n-dimensional vector exists... Makes the following inequalities true:

[0052]

[0053]

[0054]

[0055] A p +θ2B p K p A p +l max B p K p A p +d max B p K p and It is a Metzler matrix;

[0056] Where p∈S, 0 represents a vector or matrix of appropriate dimensions. Representation matrix The smallest eigenvalue, and Then, under the distributed control protocol in step S3.1, the global error variable of the communication network system, i.e., equation (6), can achieve positivity and consistency between the leader and the followers.

[0057] S4. For the established model, framework, and designed control protocol, perform positive constraint verification on the system. The specific verification process is as follows:

[0058] S4.1 First, according to the conditions in step S3.2, the lower bound of equation (6) in step S3.3 is:

[0059]

[0060] S4.2 The positive analysis of the lower bound of the global error variable in step S4.1 above is as follows: When ξ(t0)=ξ0(t0)=0 or γ(t0)=1, according to the conditions given in step S3.4, A p Or A p +θ2B p K p It is a Metzler matrix.

[0061] When ξ(t0) = 1 and ξ0(t0) = γ(t0) = 0, we have

[0062]

[0063] According to the conditions in step S3.4, A can be obtained. p +l max B p K p It is a Metzler matrix. Because... and Easy to obtain Combining the conditions given in step S3.4 and the specific design of the controller gain in step S3.1, it can be seen that... Therefore, we can obtain It is a Metzler matrix.

[0064] When ξ(t0)=γ(t0)=0 and ξ0(t0)=1, we can obtain

[0065]

[0066] According to the conditions in step S3.4, A can be obtained. p +d max B p K p It is a Metzler matrix. Therefore, It is a Metzler matrix.

[0067] When ξ(t0)=ξ0(t0)=1 and γ(t0)=0, it can be known that

[0068]

[0069] Easy to obtain and only when When it is a Metzler matrix, It is the Metzler matrix. Using the conditions in step S3.4, we have... for By using recursive derivation, it is proved that the lower bound of the global error variable (i.e., equation (8)) is positive.

[0070] S5. Verify the consistency of the communication network system under random packet loss and spoofing attacks. The specific process is as follows:

[0071] S5.1 First, under the assumptions of step S3.2, the upper bound of equation (6) in step S3.3 is:

[0072]

[0073] S5.2 Construct the following stochastic copositive Lyapunov function for the error system of communication networks:

[0074]

[0075] Its weak infinitesimal operator is:

[0076]

[0077] Applying Dynkin's formula to the above equation (11), we obtain...

[0078]

[0079] Further obtain

[0080]

[0081] Combining the last inequality in step S3.4 and the specific design form of the controller gain in step S3.1, we have:

[0082]

[0083] Finally, according to the second inequality in step S3.4, we can obtain:

[0084]

[0085] Therefore, the upper bound of the global error variable in step S5.1 (i.e., equation (9)) is stochastically stable. In other words, the communication network system model established in step S1 can guarantee the consistency of the leader and followers of the communication network nodes under the distributed control protocol based on random packet loss and spoofing attacks in step S3.1 (i.e., equation (4)).

[0086] This invention addresses the issues of data packet loss and security in data transmission within communication network systems. It provides a consistency control method for secure operation of the communication network. This method establishes a state-space model of the communication network system to address data transmission issues at network nodes, analyzes its positive constraints and the consistency between leaders and followers, and designs a distributed control protocol based on random packet loss and spoofing attacks. This ensures the secure operation of the communication network system, enabling secure and stable data transmission, thereby maximizing the utilization of network system resources.

[0087] The preferred embodiments of the present invention have been described in detail above. However, it should be understood that after reading the above teachings, those skilled in the art can make various alterations or modifications to the present invention. These equivalent forms also fall within the scope defined by the appended claims.

Claims

1. A consistency control method for secure operation of a communication network, characterized in that... The method includes the following steps: S1. Analyze the dynamic changes during the operation of the communication network, construct a communication relationship graph by taking the communication network nodes as a set of points and the communication states of data interaction between the communication network nodes as an edge set, and establish a multi-agent system model. S2. Construct a framework for random packet loss during the operation of a communication network; S3. Design a consistency control protocol for communication networks in the event of a deception attack. S4. Verify the positive constraints of the system based on the established model, framework, and designed control protocol. S5. Verify the consistency of the communication network system under random packet loss and spoofing attacks; The specific process of S1 is as follows: S1.1 Treat each communication network node as an independent agent, and establish the communication network operation process as a multi-agent system. Use graph theory to obtain the communication topology graph of the multi-agent system, and obtain its adjacency matrix and Laplace matrix, as follows: To represent the relationships between communication nodes in a communication network, a graph is introduced. ,in, for N One communication network node, For edge set, It is an adjacency matrix. For communication network nodes and nodes The communication status; if communication is possible, then... ,otherwise Record the communication network nodes The set of neighbor nodes is ; According to the topology diagram Laplace matrix and , The information exchange matrix for consistency control is defined as follows: ,in, Let be a diagonal matrix, representing the constraint gain of the leader's communication network nodes on the follower's communication network nodes. If the communication network nodes... If information can be received from the leader's communication network nodes, then ,otherwise ; S1.

2. Collect data based on the state changes of communication network nodes and establish a state-space model of communication data in the communication network system, in the following form: ， in, express t Real-time communication network nodes i Data transmission volume, n Indicates the number of channels. yes t Time of the first The number of data packets sent from the control center in each communication network node; and They represent Given the system matrix and input matrix under modal conditions, by t Data is obtained from sensor acquisition during the actual data packet transmission process at any given moment; let That is, and ;function Indicates time dependence t The piecewise constant function is a semi-Markov transition signal that randomly switches between multiple subnets in a communication network system. Its update process follows the following probability transition: ， in, It is the residence time of the transfer rate; for all They all Established, It is a time increment. yes Higher-order terms; Indicates from subnet arrive The transfer rate, and ,as well as The mathematical expectation is ; S1.

3. Based on the operational characteristics of the communication network, one of the communication network nodes is defined as the leader agent, and its dynamic equation is as follows: ， in, Represents the state of the leader agent, with initial conditions as follows: ; S1.4, Communication Network Node The leader agent must satisfy the following consistency condition: 。 2. The consistency control method for secure operation of communication networks according to claim 1, characterized in that, The random packet loss framework in S2 is constructed as follows: in, and They represent Real-time communication network nodes To communication network node The actual amount of data transmitted and the number of communication network nodes To the leader communication network node The actual amount of data transmitted; random variables and These represent data packet loss in the follower-follower and leader-follower communication network nodes, respectively; assuming... and And there are , , , ,in, and All are expected values ​​and satisfy , .

3. The consistency control method for secure operation of communication networks according to claim 2, characterized in that, S3 is as follows: S3.1 The design form of the consistency control protocol for operating the communication network under spoofing attack conditions is as follows: in, Indicates a deception attack variable. Represents independence and Bernoulli random variables satisfying , , The expected value and satisfied ; The controller gain that needs to be designed is, in the following form: ， in, , , , for dimensional vector, for dimensional vector, It is the transpose symbol; S3.

2. For the nonlinear attack signal in S3.1, establish the following sector conditions: ， in, and Given a positive constant and satisfying The initial value satisfies ; S3.

3. Based on the communication topology, introduce error variables: The global error variable can be obtained by combining the system model and distributed protocol established in the above steps, and its form is as follows: ， Among them, symbols Indicates the Kronecker product. express A column vector whose elements are all 1; for The compact form, namely , This indicates that follower agents and leader agents are in The time derivative of the compact form of the communication data error variable at a given time. for Deceiving attack variables at all times The compact form, namely ; S3.4 The conditions for secure data transmission in a communication network system under deception attacks are designed as follows: Design constants , , If it exists dimensional vector , , Makes the following inequalities true: ， ， ， , , and It is a Metzler matrix, where, , Represents a vector or matrix of appropriate dimensions. Representation matrix The smallest eigenvalue, , , , ; Then, under the S3.1 distributed control protocol, the communication network system can achieve consistency between the positive and leader agents and the follower agents.

4. The consistency control method for secure operation of communication networks according to claim 3, characterized in that, The verification process for positive constraints in S4 is as follows: S4.1 According to the conditions in S3.2, the lower bound of the global error variable in S3.3 is: S4.2, The positive analysis of the system is as follows: When or At that time, according to the conditions given in S3.4, it can be known that or It is a Metzler matrix; when and Sometimes, because , ,and Easy to obtain ; Based on the conditions given in S3.4 and the specific design of the controller gain in S3.1, it can be seen that... ;and then It is a Metzler matrix; when and At that time, it can be obtained According to the conditions in S3.4, we know that It is a Metzler matrix; therefore, It is a Metzler matrix; when and At that time, it can be known Easy to obtain and only when When it is a Metzler matrix, It is a Metzler matrix; Using the conditions in S3.4, we have ; for By using recursive derivation, it is proved that the lower bound of the global error variable of the communication network is positive.

5. The consistency control method for secure operation of communication networks according to claim 4, characterized in that, S5 includes the following steps: S5.1 Under the assumptions of S3.2, the upper bound of the global error variable in S3.3 is: S5.2 Construct the following stochastic copositive Lyapunov function for the error variables of the communication network: Its weak infinitesimal operator is: Applying Dynkin's formula to the above equation, we obtain: Further obtain Combined with the conditions in S3.4 The specific design forms of the controller gain in S3.1 include: Finally, according to the second inequality in S3.4, we can obtain: Therefore, the upper bound of the global error variable in S5.1 is stochastically stable, which verifies the consistency of the communication network system under random packet loss and spoofing attacks.

Images