Method, apparatus, processor and storage medium for determining devices in a network

Abstract

This invention discloses a method, apparatus, processor, and storage medium for determining devices in a network. The method includes: acquiring node information of a target network, wherein the node information at least represents the location information of different devices in the target network; establishing a graph structure model of the target network based on the node information, wherein the graph structure model includes multiple nodes and at least one edge, where nodes represent devices and edges represent relationships between different devices; determining the fitness of each node in the graph structure model based on the graph structure model, wherein the fitness indicates the importance of a node in the graph structure model; and determining at least one target device in the target network based on the fitness, wherein the target device affects the security status of the target network's functionality. This invention solves the technical problem of being unable to determine critical devices in a network.

Description

Technical Field

[0001] This invention relates to the field of node detection technology, and more specifically, to a method, apparatus, processor, and storage medium for determining devices in a network. Background Technology

[0002] Currently, the Internet is composed of tens of thousands of Autonomous Systems (AS). These ASs are the basic building blocks of the network. Routers within an AS use a unified routing protocol, and different ASs exchange routing information through inter-domain routing protocols. The inter-domain routing system is the backbone of the Internet, undertaking the core function of data exchange. An attack on this system can have a cascading effect, significantly impacting the entire Internet. To defend against such attacks, it is necessary to identify a set of critical devices within the inter-domain routing system and implement targeted defensive measures to reduce the damage caused by the attack.

[0003] Under normal circumstances, existing technologies struggle to accurately estimate the impact of simultaneous failures of multiple devices on the entire network. Furthermore, the site selection strategies of existing technologies are relatively simple, making it difficult to effectively identify critical device sets. Therefore, there is a technical problem of being unable to determine the critical devices in the network.

[0004] There is currently no effective solution to the aforementioned technical problem of being unable to identify key devices in the network. Summary of the Invention

[0005] This invention provides a method, apparatus, processor, and storage medium for determining devices in a network, to at least address the technical problem of being unable to determine key devices in a network.

[0006] According to one aspect of the present invention, a method for determining devices in a network is provided. The method may include: acquiring node information of a target network, wherein the node information is used to at least represent the location information of different devices in the target network; establishing a graph structure model of the target network based on the node information, wherein the graph structure model includes multiple nodes and at least one edge, where nodes represent devices and edges represent relationships between different devices; determining the fitness of each node in the graph structure model based on the graph structure model, wherein the fitness is used to indicate the importance of a node in the graph structure model; and determining at least one target device in the target network based on the fitness, wherein the target device affects the security status of the target network's functions.

[0007] Optionally, based on the graph structure model, the fitness of each node in the graph structure model is determined, including: determining at least one failed node in the graph structure model, wherein the failed node is used to indicate that the node is in a failed state; and determining the fitness of each node in the graph structure model based on the failed node.

[0008] Optionally, based on the failed nodes, the fitness of each node in the graph structure model is determined, including: determining the influence of the failed nodes on the graph structure model, wherein the influence is used to represent the degree of influence of the failed nodes on the graph structure model; determining the node group based on the influence, wherein the node group is used to indicate the set of nodes; and determining the fitness of each node in the graph structure model based on the node group.

[0009] Optionally, based on the node group, the fitness of each node in the graph structure model is determined, including: calling an evaluation function to determine the fitness of each node in the node group, wherein the evaluation function is used to evaluate the fitness of the nodes.

[0010] Optionally, the method for determining devices in the network further includes: simultaneously identifying multiple nodes as failed nodes; and using an evaluation function to determine the fitness of the multiple failed nodes.

[0011] Optionally, determining at least one target device in the target network based on fitness includes: updating the node group in response to a fitness value being less than a fitness threshold; determining each node in the updated node group as a target node in the graph structure model; and determining the device corresponding to the target node as a target device in the target network.

[0012] According to another aspect of the present invention, an apparatus for determining devices in a network is also provided. The apparatus may include: an acquisition unit for acquiring node information of a target network, wherein the node information is used to at least represent the location information of different devices in the target network; an establishment unit for establishing a graph structure model of the target network based on the node information, wherein the graph structure model includes multiple nodes and at least one edge, nodes representing devices and edges representing relationships between different devices; a first determination unit for determining the fitness of each node in the graph structure model based on the graph structure model, wherein the fitness is used to indicate the importance of a node in the graph structure model; and a second determination unit for determining at least one target device in the target network based on the fitness, wherein the target device affects the security status of the target network's functions.

[0013] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is run by a processor, it controls the device where the storage medium is located to execute the method for determining a device in a network according to the present invention.

[0014] According to another aspect of the present invention, a processor is also provided. The processor is used to run a program, wherein the program, when running, executes the method for determining devices in a network according to the embodiments of the present invention.

[0015] According to another aspect of the present invention, a computer program product is also provided. The program product includes computer instructions that, when executed by a processor, implement the method for determining devices in a network according to the embodiments of the present invention.

[0016] In this embodiment of the invention, node information of the target network is obtained, wherein the node information is used to at least represent the location information of different devices in the target network; based on the node information, a graph structure model of the target network is established, wherein the graph structure model includes multiple nodes and at least one edge, where nodes represent devices and edges represent relationships between different devices; based on the graph structure model, the fitness of each node in the graph structure model is determined, wherein fitness is used to indicate the importance of a node in the graph structure model; based on the fitness, at least one target device in the target network is determined, wherein the target device affects the security status of the target network function. In other words, this invention establishes a graph structure model of the target network and determines the target device in the target network based on the graph structure model. Because this invention utilizes the network topology information to calculate the impact of node failure on the entire network, it identifies potentially destructive nodes in the network, thereby determining key devices that have a significant impact on the network, solving the technical problem of being unable to determine key devices in the network, and achieving the technical effect of determining key devices in the network. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a method for determining a device in a network according to an embodiment of the present invention;

[0019] Figure 2 This is a flowchart of a key node detection method for an inter-domain routing system based on particle swarm optimization according to an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of a device for determining a device in a network according to an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, functional component, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, functional components, or devices.

[0023] According to an embodiment of the present invention, an embodiment of a method for determining a device in a network is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0024] Figure 1 This is a flowchart of a method for determining a device in a network according to an embodiment of the present invention, such as... Figure 1 As shown, the method may include the following steps:

[0025] Step S101: Obtain node information of the target network.

[0026] In the technical solution provided by step S101 of the present invention, the node information is used to at least represent the location information of different devices in the target network. The target network can also be referred to as a network or an inter-domain routing system.

[0027] In this embodiment, node information of the target network is obtained. For example, node information of the target network can be obtained through a management tool. This is merely an example and does not limit the specific method for obtaining node information of the target network.

[0028] For example, management tools can be used to obtain information about routers in the network and their routing relationships.

[0029] Step S102: Based on node information, establish a graph structure model of the target network.

[0030] In the technical solution provided by step S102 of the present invention, the graph structure model includes multiple nodes and at least one edge. Nodes are used to represent devices, and edges are used to represent the relationship between different devices.

[0031] In this embodiment, after obtaining the node information of the target network in step S101, a graph structure model of the target network is established based on the node information.

[0032] For example, routers and their routing relationships in a network can be modeled as a graph structure, where nodes represent routers and edges represent routing relationships between two routers.

[0033] Step S103: Based on the graph structure model, determine the fitness of each node in the graph structure model.

[0034] In the technical solution provided by step S103 of the present invention, fitness is used to indicate the importance of a node in a graph structure model.

[0035] In this embodiment, after establishing the graph structure model of the target network in step S102, the fitness of each node in the graph structure model is determined based on the graph structure model.

[0036] Optionally, at least one failed node in the graph structure model is identified, where a failed node represents a node in a failed state, that is, the device in the network corresponding to the failed node is in a failed state. In other words, the impact of a single node failure on the entire network is calculated.

[0037] Optionally, after identifying the failed nodes, the degree of influence of the failed nodes on the graph structure model is determined; based on the degree of influence, a node group is determined, whereby the node group is used to indicate the set of nodes. The node group can also be a particle swarm.

[0038] Optionally, after determining the node group, the fitness of each node in the graph structure model is determined based on the node group and the failed nodes. Fitness can also be referred to as importance.

[0039] For example, we obtain an evaluation function, which is used to evaluate the fitness of nodes; using the evaluation function, we determine the fitness of each node in the node group. The evaluation function can also be called the fitness function F.

[0040] For another example, the fitness function F is designed through load redistribution and UPDATE message propagation. The specific structure of the fitness function is as follows: Load redistribution: When a router in the network fails, the load originally borne by that router will be transferred to its neighboring router. If the original load borne by the router was large and the neighboring router can only bear a small load, the neighboring router will fail after sharing the traffic of the failed router. To evaluate the impact of node failure on the entire network in this case, the evaluation function F1(S) is used to evaluate the impact of a group of nodes S failing simultaneously on the network. The evaluation function F1(S) can be expressed by the following formula (1):

[0041]

[0042] Among them, |SP i | represents the number of optimal routing paths through node i, and |SP| represents the number of optimal routing paths in the entire network. UPDATE message propagation: After a route fails, an UPDATE message will be sent to neighboring routers. This type of message increases the load on neighboring routers, and in extreme cases, it can cause neighboring routers to fail. To evaluate the impact of a group of nodes S failing simultaneously on the network under this condition, this invention designs an evaluation function F2(S), which can be expressed by the following formula (3):

[0043]

[0044] in, Let S be the set of neighboring nodes, p be the probability that a neighboring node is affected, and τ(i) be the number of neighboring nodes of S. An effective fitness function F is designed by combining F1 and F2 to evaluate the importance of the node set S. Its calculation method is shown in the following formula (3), which will not be elaborated here.

[0045] F(S)=F1(S)+F2(S) (3)

[0046] Among them, the fitness function F is used to evaluate all particles. The larger the value of the fitness function F, the higher the quality of the solution.

[0047] Step S104: Based on fitness, determine at least one target device in the target network.

[0048] In the technical solution provided in step S104 of the present invention, the target device affects the security status of the target network function. The target device can be referred to as a critical device; damage or attack on the critical device can lead to severe impairment or paralysis of the target network function.

[0049] In this embodiment, after determining the fitness of each node in the graph structure model in step S103, the fitness is used to determine at least one target device in the target network.

[0050] Optionally, when the fitness is less than the fitness threshold, it indicates that the fitness of the node is low, that is, the node has little impact on the network. Based on this, the node group can be updated.

[0051] For example, the impact of a single node failure on the entire network can be calculated using the following formula (4):

[0052]

[0053] Among them, |SPi | represents the number of optimal routes passing through node i, |SP| represents the number of optimal routes in the entire network, and |AS i | represents the number of autonomous systems (AS) to which the neighboring nodes of node i belong, and |AS| represents the total number of AS in the network. Then, the k nodes obtained by the roulette wheel selection method are used to initialize the position vector of the i-th particle. The calculation of the impact of a single node failure on the entire network is repeated until the position vector of each particle is initialized.

[0054] Optionally, each node in the updated node group is identified as a target node in the graph structure model; and the device corresponding to the target node is identified as a target device in the target network.

[0055] For example, based on the calculated fitness function value of each particle, the historical best position Pbest of each particle is updated, and the historical best fitness function value of the i-th particle is also saved. The historical best position of the i-th particle is represented as shown in the following formula (5):

[0056]

[0057] Based on the obtained optimal fitness function value of each particle, the historical optimal position Gbest of the particle swarm is updated, and the historical optimal fitness function value of the particle swarm is also saved. The historical optimal position of the particle swarm is represented as shown in the following formula (6):

[0058] Gbest=(gbest 1 gbest 2 ,…,gbest k (6)

[0059] It should be noted that the above embodiments can be executed by a device determination mechanism in the network.

[0060] In steps S101 to S104 of this invention, node information of the target network is obtained, wherein the node information is used to at least represent the location information of different devices in the target network; based on the node information, a graph structure model of the target network is established, wherein the graph structure model includes multiple nodes and at least one edge, where nodes represent devices and edges represent relationships between different devices; based on the graph structure model, the fitness of each node in the graph structure model is determined, wherein fitness is used to indicate the importance of a node in the graph structure model; based on the fitness, at least one target device in the target network is determined, wherein the target device affects the security status of the target network function. In other words, this invention establishes a graph structure model of the target network and determines the target device in the target network based on the graph structure model. Because this invention utilizes the network topology information to calculate the impact of node failure on the entire network, it identifies potentially destructive nodes in the network and thus determines key devices that have a significant impact on the network, solving the technical problem of being unable to determine key devices in the network and achieving the technical effect of determining key devices in the network.

[0061] The method described in this embodiment will be further described below.

[0062] As an optional implementation method, the fitness of each node in the graph structure model is determined based on the graph structure model, including: determining at least one failed node in the graph structure model, wherein the failed node is used to indicate that the node is in a failed state; and determining the fitness of each node in the graph structure model based on the failed node.

[0063] In this embodiment, at least one failed node in the graph structure model is identified, and the fitness of each node in the graph structure model is determined based on the failed node.

[0064] Optionally, indicators designed for the failure of a single node can improve the accuracy of acquiring target devices, thereby solving the technical problem of being unable to identify key devices in the network, and thus achieving the technical effect of identifying key devices in the network.

[0065] As an optional implementation method, the fitness of each node in the graph structure model is determined based on the failed node, including: determining the influence of the failed node, wherein the influence is used to represent the degree of influence of the failed node on the graph structure model; determining the node group based on the influence, wherein the node group is used to indicate the set of nodes; and determining the fitness of each node in the graph structure model based on the node group.

[0066] In this embodiment, the degree of influence of the failed node on the graph structure model is determined; based on the degree of influence, a node group is determined; and based on the node group, the fitness of each node in the graph structure model is determined.

[0067] Optionally, the impact of a single node failure on the entire network can be calculated, and the calculation method is as shown in the aforementioned formula (4), which will not be repeated here.

[0068] As an optional implementation method, the fitness of each node in the graph structure model is determined based on the node group, including: calling an evaluation function to determine the fitness of each node in the node group, wherein the evaluation function is used to evaluate the fitness of the nodes.

[0069] In this embodiment, an evaluation function is obtained, and the fitness of each node in the node group is determined using the evaluation function.

[0070] For example, the particle swarm optimization algorithm is used to determine the target device. In the particle swarm optimization algorithm, the fitness function F is used to evaluate all particles and obtain the fitness function value of each particle. The larger the fitness function value of a particle, the higher the quality of the solution it represents. The fitness function can be composed of load redistribution and UPDATE message propagation. An effective fitness function F is designed to evaluate the importance of the node set S. Its calculation method is shown in the aforementioned formula (3), which will not be repeated here.

[0071] As an optional implementation method, the method for determining devices in the network further includes: simultaneously identifying multiple nodes as failed nodes; and using an evaluation function to determine the fitness of the multiple failed nodes.

[0072] In this embodiment, multiple nodes are simultaneously identified as failed nodes; the fitness of the multiple failed nodes is determined using an evaluation function.

[0073] Optionally, indices designed for multiple node failures can improve the accuracy of identifying target devices, thereby solving the technical problem of being unable to determine key devices in the network, and ultimately achieving the technical effect of identifying key devices in the network. That is, in the particle swarm optimization algorithm, indices designed for multiple node failures are integrated into the population search phase, guiding the evolution of the population and effectively improving the efficiency of searching for high-quality solutions.

[0074] As an optional embodiment, determining at least one target device in the target network based on fitness includes: updating the node group in response to a fitness value being less than a fitness threshold; determining each node in the updated node group as a target node in the graph structure model; and determining the device corresponding to the target node as a target device in the target network.

[0075] In this embodiment, when the fitness is less than the fitness threshold, the node group is updated; each node in the updated node group is determined as the target node in the graph structure model; and the device corresponding to the target node is determined as the target device in the target network.

[0076] For example, based on the calculated historical best position Pbest for each particle and the historical best position Gbest for the particle swarm, the velocity and position of each particle are updated. For the velocity update, the velocity vector of each particle in the swarm is updated, and the velocity vector update rule for the i-th particle is shown in the following formula (7):

[0077] V i ←H(ωV i +c1r1(Pbest i ∩X i )+c2r2(Gbest∩X i (7) where r1 and r2 represent random numbers belonging to the interval (0,1). Assume H(Z) = (h(z1),h(z2),...,h(z...)). k )), h(z i The expression for ) is shown in the following formula (8):

[0078]

[0079] Assuming Z = (3,1,5,0,-1), according to the above rules, we can obtain H(Z) = (1,0,1,0,0), which indicates that the first and third elements of the particle position vector need to be replaced. The update rule for particle position updates is to update the position vector of each particle based on the updated velocity vector obtained from the particle velocity update. The position vector update rule for the i-th particle is shown in the following formula (9):

[0080] X i ←X i ⊕V i (9)

[0081] Among them, X i ⊕V i =(x i ′ 1,x i ′ 2,…,x i ′ k ), x i ′ j The update rule is shown in the following formula (10):

[0082]

[0083] Where Rand(N) represents randomly selecting a node from all nodes. Assuming X1 = (6, 14, 9, 10, 45) and V1 = (0, 1, 0, 0, 1), according to the above rules, X... ′1 = (6,3,9,10,56), this vector indicates that the 2nd and 5th elements of the particle position vector need to be replaced, while the remaining elements remain unchanged.

[0084] It should be noted that the above embodiments can be executed by a device determination mechanism in the network.

[0085] In this embodiment, node information of the target network is obtained, wherein the node information is used to at least represent the location information of different devices in the target network; based on the node information, a graph structure model of the target network is established, wherein the graph structure model includes multiple nodes and at least one edge, where nodes represent devices and edges represent relationships between different devices; based on the graph structure model, the fitness of each node in the graph structure model is determined, wherein fitness is used to indicate the importance of a node in the graph structure model; based on the fitness, at least one target device in the target network is determined, wherein the target device affects the security status of the target network function. In other words, this invention establishes a graph structure model of the target network and determines the target device in the target network based on the graph structure model. Because this invention utilizes the network topology information to calculate the impact of node failure on the entire network, it identifies potentially destructive nodes in the network, thereby determining key devices that have a significant impact on the network, solving the technical problem of being unable to determine key devices in the network, and achieving the technical effect of determining key devices in the network.

[0086] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.

[0087] Currently, the Internet is composed of tens of thousands of Autonomous Systems (AS). These ASs are the basic building blocks of the network. Routers within an AS use a unified routing protocol, and different ASs exchange routing information through inter-domain routing protocols. The inter-domain routing system is the backbone of the Internet, undertaking the core function of data exchange. An attack on this system can have a cascading effect, significantly impacting the entire Internet. To defend against such attacks, it is necessary to identify a set of critical devices within the inter-domain routing system and implement targeted defensive measures to reduce the damage caused by the attack.

[0088] Under normal circumstances, existing technologies struggle to accurately estimate the impact of simultaneous failures of multiple devices on the entire network. Furthermore, the selection strategies of existing technologies are relatively simple, making it difficult to effectively identify critical device sets. Therefore, there is a technical problem of being unable to determine critical devices in the network. Currently, no effective solution has been proposed to address this technical problem of being unable to determine critical devices in the network.

[0089] However, this invention proposes a method for detecting critical nodes in inter-domain routing systems based on particle swarm optimization. By establishing a graph structure model of the network and designing an evaluation function according to the particle swarm algorithm, the impact of single and multiple failed nodes on the network is evaluated to determine the final particle swarm and critical devices. This solves the technical problem of being unable to determine critical devices in the network and achieves the technical effect of determining critical devices in the network.

[0090] The embodiments of the present invention will be further described below.

[0091] Figure 2 This is a flowchart illustrating a key node detection method for an inter-domain routing system based on particle swarm optimization (PSO) according to an embodiment of the present invention. PSO is a population-based stochastic optimization technique that can simultaneously search multiple regions in the solution space of the objective function to be optimized. The key node detection method includes the following steps:

[0092] Step S201: Initialize the particle swarm.

[0093] In this embodiment, the particle swarm size m, the number of key nodes k, the inertia weight ω, the learning factors c1 and c2, and the number of iterations T are initialized.

[0094] Optionally, the impact of a single node failure on the entire network can be calculated, and the calculation method is as shown in the aforementioned formula (4), which will not be repeated here.

[0095] Optionally, initialize the velocity vector V of the particle swarm; initialize the velocity vector of the i-th particle to a vector of all zeros, i.e., Vi. i = (0,0,…,0). Repeat the initialization of the particle swarm's velocity vector until the velocity vector of each particle is initialized.

[0096] Step S202: Calculate the fitness value for each particle.

[0097] In this embodiment, a fitness function F is used to evaluate all particles, resulting in a fitness function value for each particle. The higher the fitness function value of a particle, the higher the quality of the solution it represents.

[0098] Optionally, the fitness function is specifically constructed as follows: Load redistribution occurs when a router in the network fails. The load originally borne by that router is transferred to its neighboring routers. If the original load borne by the router is large, and the neighboring routers can only handle a small load, the neighboring routers will share the traffic of the failed router, leading to the failure of the neighboring routers. To evaluate the impact of node failure on the entire network in this situation, the evaluation function F1(S) is used to evaluate the impact of a group of nodes S failing simultaneously on the network. The evaluation function F1(S) can be expressed by the aforementioned formula (1), which will not be elaborated here.

[0099] Optionally, UPDATE message propagation: After a route fails, an UPDATE message will be sent to the neighboring router. This type of message will increase the load on the neighboring router, and in extreme cases, it may cause the neighboring router to fail. To evaluate the impact of a group of nodes S failing simultaneously on the network under this situation, the present invention designs an evaluation function F2(S), which can be expressed by the aforementioned formula (2), and will not be elaborated here.

[0100] Optionally, an effective fitness function F can be designed by combining F1 and F2 to evaluate the importance of the node set S. The calculation method is shown in the aforementioned formula (3), which will not be repeated here.

[0101] Step S203: Update the historical best position for each particle.

[0102] In this embodiment, the historical best position Pbest of each particle is updated based on the fitness function value of each particle calculated in step S202, and the historical best fitness function value of the i-th particle is also saved. The historical best position of the i-th particle is represented as shown in the aforementioned formula (5), and will not be repeated here.

[0103] Step S204: Update the historical best position of the group.

[0104] In this embodiment, the historical best position Gbest of the particle swarm is updated based on the optimal fitness function value of each particle obtained in step S203, and the historical best fitness function value of the particle swarm is also saved. The historical best position of the particle swarm is represented as shown in the aforementioned formula (6), and will not be repeated here.

[0105] Step S205: Update the velocity and position of each particle.

[0106] In this embodiment, the velocity and position of each particle are updated based on the historical best position Pbest of each particle and the historical best position Gbest of the particle swarm calculated in steps S203 and S204.

[0107] Optionally, for particle velocity updates, the velocity vector of each particle in the swarm is updated. The velocity vector update rule for the i-th particle is as shown in the aforementioned formula (7), and will not be repeated here. h(z i The expression for H(Z) is shown in the aforementioned formula (8), and will not be repeated here. According to the above rules, we can obtain H(Z) = (1,0,1,0,0), which means that the first and third elements of the particle position vector need to be replaced.

[0108] Optionally, the update rule for updating the particle's position is to update the position vector of each particle based on the updated velocity vector obtained from the particle's velocity update. The update rule for the position vector of the i-th particle is as shown in the aforementioned formula (9), and will not be repeated here. i ′ j The update rule is as shown in the aforementioned formula (10), and will not be repeated here. Assuming X1 = (6,14,9,10,45) and V1 = (0,1,0,0,1), X can be obtained according to the above rule. ′ 1 = (6,3,9,10,56), this vector indicates that the 2nd and 5th elements of the particle position vector need to be replaced, while the remaining elements remain unchanged.

[0109] Step S206: Is the termination condition met?

[0110] In this embodiment, it is determined whether the termination condition is met. The termination condition can be reaching the maximum number of iterations. If the condition is met, the detection process ends; if not, step S202 is executed.

[0111] Optionally, this method utilizes the network topology information of the inter-domain routing system to design a mechanism for evaluating the impact of a single node failure on the entire network, thereby identifying potentially destructive nodes in the network. Considering that simultaneous failure of multiple nodes can trigger load redistribution and UPDATE message propagation, this method utilizes network topology information to design an effective fitness function to evaluate particle quality. The fitness function plays a crucial role in the feasible solution search phase.

[0112] Optionally, this method transforms the critical node detection problem in inter-domain routing systems into a single-objective optimization problem, effectively solving it from an optimization perspective. During the population initialization phase, metrics designed for individual node failures are incorporated to obtain a set of high-quality solutions. During the population search phase, metrics designed for multiple node failures are incorporated, guiding the population's evolution and effectively improving the efficiency of searching for high-quality solutions.

[0113] Optionally, by reading and parsing relevant data from two Autonomous Systems (AS), two realistic AS network topologies (AS-1 and AS-2) were constructed. Table 1 shows the AS network topology data. As shown in Table 1, AS-1 contains 2317 nodes and 3589 edges, while AS-2 contains 4078 nodes and 8901 edges. Experiments demonstrate that RS-PSO can identify more critical routing nodes in the inter-domain routing system compared to the random selection method RAND.

[0114] Table 1 AS Network Topology Data Table

[0115]

[0116] In this embodiment, a graph structure model of the network is established, and an evaluation function is designed based on the particle swarm optimization algorithm to assess the impact of single and multiple failed nodes on the network, thereby determining the final particle swarm and key devices. This solves the technical problem of being unable to determine key devices in the network and achieves the technical effect of identifying key devices in the network.

[0117] According to embodiments of the present invention, a device for determining devices in a network is also provided. It should be noted that this device for determining devices in a network can be used to execute the method for determining devices in a network described in Embodiment 1.

[0118] Figure 3 This is a schematic diagram of a device for determining a device in a network according to an embodiment of the present invention. Figure 3 As shown, the device determination device 300 in the network may include: an acquisition unit 301, an establishment unit 302, a first determination unit 303, and a second determination unit 304.

[0119] The acquisition unit 301 is used to acquire node information of the target network, wherein the node information is used to at least represent the location information of different devices in the target network.

[0120] Establishment unit 302 is used to establish a graph structure model of the target network based on node information. The graph structure model includes multiple nodes and at least one edge. Nodes are used to represent devices, and edges are used to represent the relationships between different devices.

[0121] The first determining unit 303 is used to determine the fitness of each node in the graph structure model based on the graph structure model, wherein the fitness is used to indicate the importance of the node in the graph structure model.

[0122] The second determining unit 304 is used to determine at least one target device in the target network based on fitness, wherein the target device affects the security status of the target network function.

[0123] Optionally, the first determining unit 303 may include: a first determining module, used to determine at least one failed node in the graph structure model, wherein the failed node is used to indicate that the node is in a failed state; and a second determining module, used to determine the fitness of each node in the graph structure model based on the failed node.

[0124] Optionally, the second determining module may include: a first determining submodule for determining the influence degree of a failed node, wherein the influence degree represents the extent to which a failed node affects the graph structure model; a second determining submodule for determining a node group based on the influence degree, wherein the node group indicates a set of nodes; and a third determining submodule for determining the fitness of each node in the graph structure model based on the node group.

[0125] Optionally, the third determination submodule can also be used to call the evaluation function to determine the fitness of each node in the node group, wherein the evaluation function is used to evaluate the fitness of the nodes.

[0126] Optionally, the device determination device 300 in the network may further include: a third determination unit for simultaneously determining multiple nodes as failed nodes; and a fourth determination unit for determining the fitness of multiple failed nodes using an evaluation function.

[0127] Optionally, the third determining submodule can also be used to update the node group in response to the fitness being less than the fitness threshold; determine each node in the updated node group as the target node in the graph structure model; and determine the device corresponding to the target node as the target device in the target network.

[0128] In this embodiment, node information of the target network is obtained, wherein the node information is used to at least represent the location information of different devices in the target network; based on the node information, a graph structure model of the target network is established, wherein the graph structure model includes multiple nodes and at least one edge, where nodes represent devices and edges represent relationships between different devices; based on the graph structure model, the fitness of each node in the graph structure model is determined, wherein fitness is used to indicate the importance of a node in the graph structure model; based on the fitness, at least one target device in the target network is determined, wherein the target device affects the security status of the target network function. In other words, this invention establishes a graph structure model of the target network and determines the target device in the target network based on the graph structure model. Because this invention utilizes the network topology information to calculate the impact of node failure on the entire network, it identifies potentially destructive nodes in the network, thereby determining key devices that have a significant impact on the network, solving the technical problem of being unable to determine key devices in the network, and achieving the technical effect of determining key devices in the network.

[0129] According to an embodiment of the present invention, a computer-readable storage medium is also provided, the storage medium including a stored program, wherein the program executes the method for determining a device in a network as described in Embodiment 1.

[0130] According to an embodiment of the present invention, a processor is also provided for running a program, wherein the program executes the method for determining devices in a network in Embodiment 1 during runtime.

[0131] According to an embodiment of the present invention, a computer program product is also provided, the computer program product including computer instructions, which, when executed by a processor, implement the method for determining devices in a network in Embodiment 1.

[0132] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0133] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0137] If the integrated unit is implemented as a software functional unit and sold or used as an independent functional component, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software functional component. This computer software functional component is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0138] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining devices in a network, characterized in that, include: Obtain node information of a target network, wherein the node information is used to at least represent the location information of different devices in the target network; Based on the node information, a graph structure model of the target network is established, wherein the graph structure model includes multiple nodes and at least one edge, the nodes are used to represent the devices, and the edges are used to represent the relationships between different devices; Based on the graph structure model, the fitness of each node in the graph structure model is determined, wherein the fitness is used to indicate the importance of the node in the graph structure model; Based on the fitness, at least one target device in the target network is identified, wherein the target device affects the security status of the target network function; Specifically, determining the fitness of each node in the graph structure model based on the graph structure model includes: Identify at least one failed node in the graph structure model, wherein the failed node is used to indicate that the node is in a failed state; Determine the influence degree of the failed node, wherein the influence degree is used to represent the degree of influence of the failed node on the graph structure model; Based on the influence degree, a node group is determined, wherein the node group is used to indicate the set of nodes; Based on the node group, the fitness of each node in the graph structure model is determined.

2. The method according to claim 1, characterized in that, Based on the node group, determining the fitness of each node in the graph structure model includes: An evaluation function is invoked to determine the fitness of each node in the node group, wherein the evaluation function is used to evaluate the fitness of the node.

3. The method according to claim 2, characterized in that, The method further includes: Multiple nodes are simultaneously identified as the failed nodes; The fitness of multiple failed nodes is determined using the evaluation function.

4. The method according to claim 1, characterized in that, Based on the fitness, determining at least one target device in the target network includes: In response to the fitness being less than the fitness threshold, the node group is updated; Each node in the updated node group is identified as a target node in the graph structure model; The device corresponding to the target node is identified as the target device in the target network.

5. A device for determining devices in a network, characterized in that, include: An acquisition unit is used to acquire node information of a target network, wherein the node information is used to at least represent the location information of different devices in the target network; A building unit is used to build a graph structure model of the target network based on the node information, wherein the graph structure model includes multiple nodes and at least one edge, the nodes are used to represent the devices, and the edges are used to represent the relationships between different devices; The first determining unit is configured to determine the fitness of each node in the graph structure model based on the graph structure model, wherein the fitness is used to indicate the importance of the node in the graph structure model; The second determining unit is configured to determine at least one target device in the target network based on the fitness, wherein the target device affects the security status of the target network function; The first determining unit includes: The first determining module is used to determine at least one failed node in the graph structure model, wherein the failed node is used to indicate that the node is in a failed state; The first determining submodule is used to determine the influence degree of the failed node, wherein the influence degree is used to represent the degree of influence of the failed node on the graph structure model; The second determining submodule is used to determine a node group based on the influence degree, wherein the node group is used to indicate the set of nodes; The third determining submodule is used to determine the fitness of each node in the graph structure model based on the node group.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program is run by a processor, it controls the device in which the storage medium is located to perform the method of any one of claims 1 to 4.

7. A computer program product, characterized in that, The computer program product includes computer instructions that, when executed by a processor, implement the method described in any one of claims 1 to 4.

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