Method and system for testing invulnerability based on dynamic heterogeneous network

Abstract

This application provides a method and system for robustness testing based on dynamic heterogeneous networks. The method includes: acquiring target data based on a multi-level target smart grid; determining the target robustness based on the target data; and, if the target robustness is determined to be less than a preset robustness, performing a target operation to make the target robustness greater than or equal to the preset robustness. The target data includes: target connectivity, target fault tolerance, target attack resistance, and / or target mortality of the multi-level target dynamic heterogeneous network. This application can improve the accuracy of perception of the dynamic heterogeneous network state, thereby improving the accuracy of robustness testing of dynamic heterogeneous networks, and ultimately improving the robustness and survivability of dynamic heterogeneous networks.

Description

Technical Field

[0001] The embodiments of this application relate to the field of network security technology, and in particular to a resilience testing method and system based on dynamic heterogeneous networks. Background Technology

[0002] Terrorism testing is primarily used to assess and improve the stability and resilience of complex networks in the face of failures, attacks, or environmental changes.

[0003] Currently, related technologies suffer from problems such as low accuracy in sensing the state of dynamic heterogeneous networks, low accuracy in testing the resilience of dynamic heterogeneous networks, and low robustness and survivability of dynamic heterogeneous networks.

[0004] Therefore, a new technical solution is urgently needed to solve the above-mentioned technical problems. Summary of the Invention

[0005] According to embodiments of this application, a robustness testing method and system based on dynamic heterogeneous networks are provided, which can improve the accuracy of perception of the state of dynamic heterogeneous networks, thereby improving the accuracy of robustness testing of dynamic heterogeneous networks, and further improving the robustness and survivability of dynamic heterogeneous networks.

[0006] In a first aspect of this application, a robustness testing method based on dynamic heterogeneous networks is proposed, comprising:

[0007] Target data is acquired based on a multi-level intelligent grid.

[0008] Determine the target's damage resistance based on the target data;

[0009] If the target damage resistance is determined to be less than the preset damage resistance, perform the target operation to make the target damage resistance greater than or equal to the preset damage resistance.

[0010] The target data includes: target connectivity, target fault tolerance, target attack resistance, and / or target death rate in a multi-level target dynamic heterogeneous network.

[0011] In some feasible implementations, the above method further includes:

[0012] Acquire target information corresponding to multi-level target dynamic heterogeneous networks;

[0013] Based on the target information, a multi-level target smart grid is generated;

[0014] The target information includes: target network topology information, target node attribute information, target service and application mapping relationship information, target security configuration information, target network operation status information, target geographical distribution information, and / or target management interface information.

[0015] In some feasible implementations, the above-mentioned multi-level target smart grid is equipped with corresponding multi-level target intelligent agents;

[0016] Among them, the first-level target intelligent grid sets up a first-level target intelligent agent to regulate the target dynamic heterogeneous network;

[0017] A secondary target intelligent grid is configured with a secondary target intelligent agent to regulate the dynamic heterogeneous sub-network of targets;

[0018] The three-level target intelligent grid is equipped with three-level target intelligent agents to regulate the dynamic heterogeneous local area network of targets.

[0019] And / or, a four-level target smart grid, setting up four-level target intelligent agents to regulate target network nodes.

[0020] In some feasible implementations, the aforementioned multi-level target intelligent agent is used to determine the corresponding target frequency information based on the corresponding target network operation status information;

[0021] Based on the target frequency information, perform target testing operations to obtain target data.

[0022] In some feasible implementations, determining the target survivability based on target data includes:

[0023] The first target resilience is determined based on the target connectivity and corresponding first weight, target fault tolerance and corresponding second weight, target attack resistance and corresponding third weight, and / or target death degree and corresponding fourth weight of the target dynamic heterogeneous network.

[0024] The second target resilience is determined based on the target connectivity and corresponding first weight, target fault tolerance and corresponding second weight, target attack resistance and corresponding third weight, and / or target death degree and corresponding fourth weight of the target dynamic heterogeneous sub-network.

[0025] The third target resilience is determined based on the target connectivity and corresponding first weight, target fault tolerance and corresponding second weight, target attack resistance and corresponding third weight, and / or target death rate and corresponding fourth weight of the target dynamic heterogeneous local area network.

[0026] The fourth target resilience is determined based on the target network node's target connectivity and corresponding first weight, target fault tolerance and corresponding second weight, target attack resistance and corresponding third weight, and / or target death rate and corresponding fourth weight.

[0027] In some feasible implementations, the third weight corresponding to the aforementioned target dynamic heterogeneous network is greater than the first weight;

[0028] Among them, the first weight, the second weight, and the fourth weight are equal;

[0029] The first and second weights corresponding to the target dynamic heterogeneous subnetwork are equal; the third and fourth weights are equal.

[0030] Among them, the fourth weight is less than the first weight;

[0031] The first weight corresponding to the target dynamic heterogeneous local area network is equal to the fourth weight; the second weight is equal to the third weight.

[0032] Among them, the third weight is less than the first weight;

[0033] The fourth weight corresponding to the target network node is greater than the first weight;

[0034] The first weight equals the second weight;

[0035] The second weight is greater than the third weight.

[0036] In some feasible implementations, the above-mentioned execution of a target operation to make the target damage resistance greater than or equal to the preset damage resistance when the target damage resistance is determined to be less than the preset damage resistance includes:

[0037] Based on the target anomaly dynamic heterogeneous network, the target anomaly dynamic heterogeneous sub-network, the target anomaly dynamic heterogeneous local network, and / or the target anomaly network nodes, construct a target anomaly digital twin model.

[0038] Generate the target fault evolution path based on the target anomaly digital twin model;

[0039] Execute the target operation based on the target fault evolution path.

[0040] In some feasible implementations, it also includes:

[0041] If the target connectivity is determined to be less than the preset connectivity, the target operations include:

[0042] Target redundant path initiation operation, target virtual node introduction operation, and / or, target topology reconstruction operation;

[0043] If the target fault tolerance is determined to be less than the preset fault tolerance, the target operations include: target node backup operation, target redundant link addition operation, and / or, target containerized microservice startup operation.

[0044] If the target's resistance to attack is determined to be less than the preset resistance to attack, the target operations include: target firewall startup, target attack source isolation, and / or target honeypot trapping.

[0045] If the target death level is determined to be greater than or equal to the preset death level, the target operations include: target edge node construction operation, target architecture decentralization operation, and / or target load migration operation.

[0046] In some feasible implementations, the target mortality rate is determined according to the following formula:

[0047]

[0048] in, Used to represent Time-based target dynamic heterogeneous network individuals The corresponding target mortality rate; Used to represent the first scaling factor; Used to represent individual targets in a dynamic heterogeneous network within historical target data. The benchmark risk; β Used to represent the second scaling factor; k Used to represent target threat factors; M Used to represent target threat factors k Quantity; Used to represent target threat factors k The corresponding weights; Used to represent Time-based target dynamic heterogeneous network individuals Target threat factors faced k The strength; Used to represent the third scaling factor; Used to represent individuals in a target dynamic heterogeneous network. A set of adjacent or related target-dynamic heterogeneous network individuals; Used to indicate ( t -1) Time-based target dynamic heterogeneous network individuals j The probability of death.

[0049] A second aspect of this application proposes a robustness testing system based on dynamic heterogeneous networks, applicable to the above-mentioned method, including:

[0050] The acquisition unit is used to acquire target data based on a multi-level target smart grid.

[0051] The determination unit is used to determine the target's damage resistance based on the target data;

[0052] An execution unit is used to perform target operations to make the target resilience greater than or equal to the preset resilience when it is determined that the target resilience is less than the preset resilience.

[0053] The target data includes: target connectivity, target fault tolerance, target attack resistance, and / or target death rate in a multi-level target dynamic heterogeneous network.

[0054] This application provides a method and system for robustness testing based on dynamic heterogeneous networks. The method includes: acquiring target data based on a multi-level target smart grid; determining the target robustness based on the target data; and, if the target robustness is determined to be less than a preset robustness, performing a target operation to make the target robustness greater than or equal to the preset robustness. The target data includes: target connectivity, target fault tolerance, target attack resistance, and / or target mortality of the multi-level target dynamic heterogeneous network. This application can improve the accuracy of perception of the dynamic heterogeneous network state, thereby improving the accuracy of robustness testing of dynamic heterogeneous networks, and ultimately improving the robustness and survivability of dynamic heterogeneous networks.

[0055] It should be understood that the description in the Summary Section is not intended to limit the key or essential features of the embodiments of this application, nor is it intended to restrict the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0056] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0057] Figure 1 A flowchart illustrating a robustness testing method based on dynamic heterogeneous networks provided in this application embodiment;

[0058] Figure 2 A structural schematic diagram of a resilience testing system based on a dynamic heterogeneous network provided in an embodiment of this application;

[0059] Figure 3 This is a structural schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0061] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0062] In a first aspect, this application proposes a resilience testing method based on dynamic heterogeneous networks, which can be applied to fields such as military intelligence, urban sensing, Internet of Things monitoring, and / or network defense. Figure 1 A flowchart illustrating a robustness testing method 100 based on dynamic heterogeneous networks provided in this application embodiment is shown below. Figure 1 As shown, method 100 includes:

[0063] Step S1: Based on the multi-level target smart grid, acquire target data; wherein, the target data includes: target connectivity, target fault tolerance, target attack resistance, and / or, target mortality of the multi-level target dynamic heterogeneous network.

[0064] For example, the target connectivity of a multi-level target dynamic heterogeneous network can be obtained based on the aforementioned multi-level target smart grid, which can be used to determine the communication capability of the multi-level target dynamic heterogeneous network.

[0065] For example, the target fault tolerance of the multi-level target dynamic heterogeneous network can be obtained based on the above-mentioned multi-level target smart grid, which can be used to determine the ability of the multi-level target dynamic heterogeneous network to maintain basic functions even when some components fail.

[0066] For example, the target attack resistance of the multi-level target dynamic heterogeneous network can be obtained based on the above-mentioned multi-level target smart grid, which can be used to determine the ability of the multi-level target dynamic heterogeneous network to resist external attacks.

[0067] For example, the target mortality of the multi-level target dynamic heterogeneous network can be obtained based on the multi-level target smart grid mentioned above, which can be used to determine the degree to which the multi-level target dynamic heterogeneous network has failed or been destroyed.

[0068] In some feasible implementations, before performing step S1, the method further includes:

[0069] Obtain target information corresponding to a multi-level target dynamic heterogeneous network; wherein, the target information includes: target network topology information, target node attribute information, target service and application mapping relationship information, target security configuration information, target network operation status information, target geographical distribution information, and / or, target management interface information.

[0070] For example, the aforementioned target network topology information may include: the number and type information of each network node corresponding to the multi-level target dynamic heterogeneous network, such as: router information, switch information, and / or, terminal device information, etc.; link connection relationship information corresponding to the multi-level target dynamic heterogeneous network, such as: adjacency matrix information, and / or, graph structure information, etc.; and / or, path weight information between each network node corresponding to the multi-level target dynamic heterogeneous network, such as: latency information, bandwidth information, and / or, packet loss rate information, etc.

[0071] For example, the target node attribute information mentioned above may include: role information of each network node corresponding to the multi-level target dynamic heterogeneous network, such as: core node information, edge node information, and / or, terminal node information, etc.; performance parameter information of each network node, such as: CPU information, memory information, and / or, storage information, etc.; redundancy or backup capability information, and / or, virtualization information, such as: container information, and / or, virtual machine information, etc.

[0072] For example, the above target service and application mapping relationship information may include: service list information running on each network node corresponding to the multi-level target dynamic heterogeneous network, such as: database information, web service information, and / or, control center information, etc.; dependency relationship information between services, such as: call chain information in microservice architecture, etc.; service quality requirement information, such as: latency information, availability information, and / or, throughput information.

[0073] For example, the aforementioned target security configuration information may include: firewall deployment information, security component information, such as intrusion detection system information; encrypted communication information, identity authentication mechanism information, vulnerability patching information, and / or attack log information, etc.

[0074] For example, the target network operation status information may include: historical fault record information, such as downtime information and recovery time information; abnormal event log information, such as traffic surge information and abnormal access information; and / or, historical values ​​of resilience assessment, etc.

[0075] For example, the aforementioned target geographic distribution information may include: geographic location information of each network node corresponding to the multi-level target dynamic heterogeneous network, such as: city information, building information, and / or, data center information, etc.; network area division information, such as: subnet information, and / or, VLAN information, etc.; and / or, cross-domain or cross-organizational collaboration information, etc.

[0076] For example, the aforementioned target management interface information may include: management protocol information, such as SSH information, SNMP information, and / or REST API information; and / or supported automation tool information, such as Ansible information, Kubernetes information, and / or SDN controller information.

[0077] Based on the target information, a multi-level target smart grid is generated.

[0078] For example, the following can be considered based on the following information: the number and type of each network node in the multi-level target dynamic heterogeneous network; the link connection relationship information in the multi-level target dynamic heterogeneous network; the path weight information between each network node in the multi-level target dynamic heterogeneous network; the role information, performance parameter information, redundancy or backup capability information, and virtualization information of each network node in the multi-level target dynamic heterogeneous network; the service list information running on each network node in the multi-level target dynamic heterogeneous network; the dependency relationship information between services; and the service quality requirement information; firewall deployment information, security component information, encrypted communication information, and identity verification information. The monitoring area is divided into several grid units to generate the above-mentioned multi-level target smart grid. The grid unit can be used to generate the above-mentioned multi-level target smart grid. The grid unit can be used to represent a logical or physical location and to record the status information, threat level information, and / or resource distribution information of the corresponding target dynamic heterogeneous network structure. The grid unit includes information such as authentication mechanism information, vulnerability patching information, attack record information; historical fault record information, abnormal event log information, abnormal access information, and historical resilience assessment value information; geographical location information, network area division information, cross-domain or cross-organizational collaboration information of each network node corresponding to the multi-level target dynamic heterogeneous network; management protocol information, and / or information on supported automation tools.

[0079] Therefore, the above method can accurately construct a multi-level target smart grid based on the target network topology information, target node attribute information, target service and application mapping relationship information, target security configuration information, target network operation status information, target geographical distribution information, and / or target management interface information. This enables hierarchical acquisition of target data, thereby improving the accuracy and efficiency of target data acquisition; furthermore, it enhances the perception accuracy of dynamic heterogeneous network status; and hierarchically executes target operations to hierarchically improve the resilience of dynamic heterogeneous networks, thereby improving the accuracy of dynamic heterogeneous network resilience testing and enhancing the robustness and survivability of dynamic heterogeneous networks.

[0080] In some feasible implementations, the aforementioned multi-level target smart grid is equipped with corresponding multi-level target intelligent agents.

[0081] Among them, the first-level target smart grid sets up a first-level target intelligent agent to regulate the target dynamic heterogeneous network.

[0082] It should be noted that the aforementioned first-level target intelligent grid sets up a first-level target intelligent agent to regulate the target dynamic heterogeneous network, i.e., the global network. This first-level target intelligent agent is used for comprehensive coordination and / or strategic planning, and possesses high computing power, communication bandwidth, and strong learning capabilities.

[0083] For example, the aforementioned first-level target smart grid with a first-level target intelligent agent can perceive the overall operational status of the target dynamic heterogeneous network based on geographic information systems and / or Internet of Things systems, perform topology optimization of the target dynamic heterogeneous network as a whole, and / or respond to major events. The aforementioned major events may include: large-scale network attack events, large-scale network outage events, and / or large-scale network failure events, etc.

[0084] Among them, the secondary target smart grid is equipped with a secondary target intelligent agent to regulate the target dynamic heterogeneous sub-network.

[0085] It should be noted that the aforementioned secondary target intelligent grid is equipped with secondary target intelligent agents for regulating the dynamic heterogeneous sub-networks, i.e., regional networks. These secondary target intelligent agents are used for sub-network management and / or task allocation, possessing moderate computing power and strong fault tolerance.

[0086] For example, the aforementioned secondary target smart grid with secondary target intelligent agents can monitor and diagnose the status of the target dynamic heterogeneous sub-network, i.e., the regional network, based on controllers, edge computing, and / or local optimization algorithms, allocate network resources of the target dynamic heterogeneous sub-network, i.e., the regional network to improve load balance, and / or detect and respond to network threats of the target dynamic heterogeneous sub-network, i.e., the regional network.

[0087] Among them, the three-level target intelligent grid is equipped with three-level target intelligent agents to regulate the dynamic heterogeneous local area network of targets.

[0088] It should be noted that the aforementioned three-level target intelligent grid is equipped with three-level target intelligent agents for regulating the target's dynamic heterogeneous local area network, i.e., the local LAN or a small-scale network. These three-level target intelligent agents are used for rapid response and / or local optimization, exhibiting low latency and high execution efficiency.

[0089] For example, the three-level target smart grid with three-level target intelligent agents described above can, based on lightweight protocols, embedded AI chips, and / or local database cached data, perform real-time control over the target dynamic heterogeneous local area network (i.e., the local LAN or small-scale network), perform real-time management of the access and permissions of network nodes in the target dynamic heterogeneous local area network (i.e., the local LAN or small-scale network), and / or perform real-time evaluation and optimization of the network quality of the target dynamic heterogeneous local area network (i.e., the local LAN or small-scale network).

[0090] And / or, wherein, a fourth-level target smart grid is configured with a fourth-level target intelligent agent to regulate target network nodes.

[0091] It should be noted that the aforementioned four-level target smart grid uses four-level target intelligent agents to regulate target network nodes, i.e., individual devices or terminals. These four-level target intelligent agents are used for status monitoring and / or simple repair of individual devices or terminals. These four-level target intelligent agents are lightweight and have low power consumption.

[0092] For example, the four-level target smart grid with four target agents described above can detect the operating status of individual target network nodes based on low-power communication protocols and / or microcontrollers; perform real-time control over the operating status of individual target network nodes, such as sleep control, wake-up control, and / or communication channel switching control; and provide local security protection for individual target network nodes, such as anti-tampering or identity authentication.

[0093] Therefore, the above method, by setting up corresponding multi-level target intelligent agents in a multi-level target intelligent grid; wherein, a first-level target intelligent grid has a first-level target intelligent agent to regulate the target dynamic heterogeneous network; a second-level target intelligent grid has a second-level target intelligent agent to regulate the target dynamic heterogeneous sub-network; a third-level target intelligent grid has a third-level target intelligent agent to regulate the target dynamic heterogeneous local area network; and / or, a fourth-level target intelligent grid has a fourth-level target intelligent agent to regulate the target network nodes, can make the regulation commands and feedback between the first-level and fourth-level target intelligent grids form a closed loop, thereby promoting vertical information collaboration between the first-level and fourth-level target intelligent grids; and enabling the target dynamic heterogeneous network at each level to have [the following characteristics] at this level. It possesses autonomous decision-making capabilities, thereby achieving horizontal autonomy for target smart grids from level one to level four; hierarchical scheduling enables optimal resource allocation for multi-level target smart grids; it reduces the impact of local anomalies on the overall multi-level target smart grid, improving its fault tolerance and anomaly isolation; it allows for hierarchical expansion of multi-level target smart grids, enhancing their scalability; it further improves the construction accuracy of multi-level target smart grids; thereby further enhancing the accuracy and efficiency of target data acquisition; it further improves the perception accuracy of dynamic heterogeneous network states, thus further enhancing the accuracy of dynamic heterogeneous network resilience testing, and further improving the robustness and survivability of dynamic heterogeneous networks.

[0094] In some feasible implementations, the aforementioned multi-level target agent is used to determine the corresponding target frequency information based on the corresponding target network operation status information; and to perform target testing operations to obtain target data based on the target frequency information.

[0095] For example, the aforementioned first-level target agent can determine the corresponding target frequency information based on the operating status information of the target dynamic heterogeneous network, i.e., the global network, and use it to perform target testing operations to obtain the target connectivity, target fault tolerance, target attack resistance, and / or target death degree of the target dynamic heterogeneous network, i.e., the global network.

[0096] For example, the aforementioned secondary target agent can determine the corresponding target frequency information based on the operational status information of the target dynamic heterogeneous sub-network, i.e., the regional network, and use it to perform target testing operations to obtain the target connectivity, target fault tolerance, target attack resistance, and / or target death degree of the target dynamic heterogeneous sub-network, i.e., the regional network.

[0097] For example, the aforementioned three-level target agent can determine the corresponding target frequency information based on the operational status information of the target dynamic heterogeneous local area network, i.e., the local LAN or small-scale network, and use it to perform target testing operations to obtain the target connectivity, target fault tolerance, target attack resistance, and / or target death rate of the target dynamic heterogeneous local area network, i.e., the local LAN or small-scale network.

[0098] For example, the aforementioned four-level target intelligent agent can determine the corresponding target frequency information based on the operating status information of the target network node, i.e., a single device or terminal, and use it to perform target testing operations to obtain the target connectivity, target fault tolerance, target attack resistance, and / or target death degree of the target network node, i.e., a single device or terminal.

[0099] It should be noted that the aforementioned Level 1, Level 2, Level 3, and / or Level 4 target agents can determine the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the network load information, service quality information, and / or security threat level information corresponding to the target network nodes, based on the aforementioned target network operation status information, and determine the aforementioned target frequency information. In order to perform corresponding target test operations on the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or target network nodes based on the aforementioned target frequency information, they can obtain the corresponding target connectivity, target fault tolerance, target attack resistance, and / or target death rate.

[0100] Specifically, the aforementioned first-level target intelligent agent can perform global network topology detection tests, key node reachability detection tests, overall bandwidth utilization monitoring tests, and / or security policy consistency checks on the target dynamic heterogeneous network based on the aforementioned target frequency information, in order to obtain the target connectivity, target fault tolerance, target attack resistance, and / or target death rate of the target dynamic heterogeneous network.

[0101] Specifically, the aforementioned secondary target intelligent agent can perform regional link quality detection tests, subnet internal node communication status tests, service load balancing analysis, and / or regional security event response mechanism verification tests on the target dynamic heterogeneous subnet based on the aforementioned target frequency information, in order to obtain the target connectivity, target fault tolerance, target anti-attack degree, and / or target mortality degree corresponding to the target dynamic heterogeneous subnet.

[0102] Specifically, the aforementioned three-level target intelligent agents can perform local area network device connection status tests, local service access tests, network congestion or conflict tests, and / or access control and access log analysis on the target dynamic heterogeneous local area network based on the aforementioned target frequency information, in order to obtain the target connectivity, target fault tolerance, target anti-attack degree, and / or target death degree corresponding to the target dynamic heterogeneous local area network.

[0103] Specifically, the aforementioned four-level target intelligent agents can perform Ping tests, port open status scan tests, CPU, memory, and disk resource usage analysis, application layer interface call tests, and / or log analysis and abnormal behavior identification on the target network nodes based on the aforementioned target frequency information, in order to obtain the target connectivity, target fault tolerance, target attack resistance, and / or target death rate of the target network nodes.

[0104] Among them, the target frequency information is positively correlated with the network load information, the service quality information, and the security threat level information. That is, the higher the network load, the higher the service quality requirement, and / or the higher the security threat level, the higher the target frequency corresponding to the execution of the target test operation.

[0105] Therefore, the above method can accurately determine the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target frequency information corresponding to the target network nodes based on the target network operation status information. This enables the first-level target agent, second-level target agent, third-level target agent, and / or fourth-level target agent to accurately execute target testing operations based on the target frequency information to determine the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target connectivity, target fault tolerance, target attack resistance, and / or target death rate corresponding to the target network nodes. This improves the accuracy of dynamic heterogeneous network resilience testing and further enhances the robustness and survivability of dynamic heterogeneous networks.

[0106] Step S2: Determine the target's damage resistance based on the target data.

[0107] For example, the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target connectivity, target fault tolerance, target attack resistance, and / or the target death rate of the target network nodes can be determined based on the aforementioned target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target annihilation resistance of the target network nodes.

[0108] In some feasible implementations, determining the target survivability based on target data includes:

[0109] The first target resilience is determined based on the target connectivity and corresponding first weight, target fault tolerance and corresponding second weight, target attack resistance and corresponding third weight, and / or target death degree and corresponding fourth weight of the target dynamic heterogeneous network.

[0110] The second target resilience is determined based on the target connectivity and corresponding first weight, target fault tolerance and corresponding second weight, target attack resistance and corresponding third weight, and / or target death degree and corresponding fourth weight of the target dynamic heterogeneous sub-network.

[0111] The third target resilience is determined based on the target connectivity and corresponding first weight, target fault tolerance and corresponding second weight, target attack resistance and corresponding third weight, and / or target death rate and corresponding fourth weight of the target dynamic heterogeneous local area network.

[0112] The fourth target resilience is determined based on the target network node's target connectivity and corresponding first weight, target fault tolerance and corresponding second weight, target attack resistance and corresponding third weight, and / or target death rate and corresponding fourth weight.

[0113] Specifically, the aforementioned first target damage resistance, second target damage resistance, third target damage resistance, and / or fourth target damage resistance can be determined according to the following formula:

[0114]

[0115] in, R Used to represent the damage resistance of the first target, the damage resistance of the second target, the damage resistance of the third target, and / or, the damage resistance of the fourth target; Used to represent the first weight; C Used to represent the target connectivity; Used to represent the second weight; F Used to indicate the target tolerance; Used to represent the third weight; A Used to indicate the target's resistance to attack; Used to represent the fourth weight; D Used to indicate the target's mortality rate.

[0116] Therefore, the above method can improve the accuracy of determining the target resilience of the target dynamic heterogeneous network, the target dynamic heterogeneous sub-network, the target dynamic heterogeneous local area network, and / or the target network node, thereby improving the execution accuracy of target operations, and further enhancing the robustness and survivability of the target dynamic heterogeneous network, the target dynamic heterogeneous sub-network, the target dynamic heterogeneous local area network, and / or the target network node.

[0117] In some feasible implementations, the third weight corresponding to the above-mentioned target dynamic heterogeneous network is greater than the first weight; wherein the first weight, the second weight, and the fourth weight are equal.

[0118] For example, the first weight of the target dynamic heterogeneous network can be set to 0.2; the second weight to 0.2; the third weight to 0.4; and the fourth weight to 0.2, so as to improve the security and anti-attack capability of the target dynamic heterogeneous network.

[0119] In some feasible implementations, the first weight and the second weight corresponding to the above-mentioned target dynamic heterogeneous sub-network are equal; the third weight and the fourth weight are equal; wherein the fourth weight is less than the first weight.

[0120] For example, the first weight of the target dynamic heterogeneous subnetwork can be set to 0.3; the second weight to 0.3; the third weight to 0.2; and the fourth weight to 0.2, so as to improve the stability and fault tolerance of the target dynamic heterogeneous subnetwork.

[0121] In some feasible implementations, the first weight corresponding to the above-mentioned target dynamic heterogeneous local area network is equal to the fourth weight; the second weight is equal to the third weight; wherein the third weight is less than the first weight.

[0122] For example, the first weight of the target dynamic heterogeneous local area network can be set to 0.4; the second weight to 0.1; the third weight to 0.1; and the fourth weight to 0.4, so as to improve the communication guarantee capability and tolerance to the failure of the target network nodes.

[0123] In some feasible implementations, the fourth weight corresponding to the target network node is greater than the first weight; the first weight is equal to the second weight; wherein, the second weight is greater than the third weight.

[0124] For example, the first weight of the target network node can be set to 0.2; the second weight to 0.2; the third weight to 0.1; and the fourth weight to 0.5, in order to reduce the influence of the failed target network node and improve the death penalty capability to increase the sensitivity to the dead target network node.

[0125] Therefore, the above method, through the differentiated settings of the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target network node corresponding to the first weight, second weight, third weight, and / or fourth weight, can further improve the accuracy of determining the target's resilience, thereby improving the execution accuracy of target operations and enhancing the robustness and survivability of the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target network node.

[0126] Step S3: If the target damage resistance is determined to be less than the preset damage resistance, perform the target operation to make the target damage resistance greater than or equal to the preset damage resistance.

[0127] For example, if the first-level target agent determines that the target resilience of the target dynamic heterogeneous network is less than the corresponding preset resilience, it can control the execution of target operations to make the target resilience of the target dynamic heterogeneous network greater than or equal to the corresponding preset resilience.

[0128] For example, if the secondary target agent determines that the target resilience of the target dynamic heterogeneous sub-network is less than the corresponding preset resilience, it can control the execution of target operations to make the target resilience of the target dynamic heterogeneous sub-network greater than or equal to the corresponding preset resilience.

[0129] For example, if the third-level target agent determines that the target resilience of the target dynamic heterogeneous local area network is less than the corresponding preset resilience, it can control the execution of target operations to make the target resilience of the target dynamic heterogeneous local area network greater than or equal to the corresponding preset resilience.

[0130] For example, if a Level 4 target agent determines that the target resilience of a target network node is less than the corresponding preset resilience, it can control the execution of target operations to make the target resilience of the target network node greater than or equal to the corresponding preset resilience.

[0131] In some feasible implementations, the above-mentioned execution of a target operation to make the target damage resistance greater than or equal to the preset damage resistance when the target damage resistance is determined to be less than the preset damage resistance includes:

[0132] Based on the target anomaly dynamic heterogeneous network, the target anomaly dynamic heterogeneous sub-network, the target anomaly dynamic heterogeneous local network, and / or the target anomaly network nodes, construct a target anomaly digital twin model; based on the target anomaly digital twin model, generate a target fault evolution path; and execute target operations based on the target fault evolution path.

[0133] For example, when a first-level target agent determines that the target resilience corresponding to the target dynamic heterogeneous network is less than the corresponding preset resilience, a second-level target agent determines that the target resilience corresponding to the target dynamic heterogeneous sub-network is less than the corresponding preset resilience, a third-level target agent determines that the target resilience corresponding to the target dynamic heterogeneous local network is less than the corresponding preset resilience, and / or a fourth-level target agent determines that the target resilience corresponding to the target network node is less than the corresponding preset resilience, a target anomaly digital twin model can be constructed based on the target fault tree, target Bayesian network, and / or target Markov chain, according to the target anomaly dynamic heterogeneous network, target anomaly dynamic heterogeneous sub-network, target anomaly dynamic heterogeneous local network, and / or target anomaly network node; based on reinforcement learning, and / or graph neural network, according to the target anomaly digital twin model, a target fault evolution path is generated to predict the fault propagation trend; and target operations are executed according to the target fault evolution path.

[0134] The aforementioned target operations may include: target resource scheduling operations, target isolation and repair operations, target redundancy deployment operations, target topology reconstruction operations, target security protection operations, and / or target early warning notification operations.

[0135] Specifically, the aforementioned target resource scheduling operation is used to migrate critical tasks to the target high-availability node. The aforementioned target isolation and repair operation is used to isolate or restart services of nodes that are about to fail. The aforementioned target redundancy deployment operation is used to add backup nodes to critical paths. The aforementioned target topology reconstruction operation is used to replan communication paths to bypass high-risk areas. The aforementioned target security protection operation is used to enhance the security of attacked nodes, such as by strengthening firewalls, strengthening encryption, and / or strengthening access control. The aforementioned target early warning notification operation is used to send alarm information to target devices, where the aforementioned target devices may include mobile devices belonging to the target operations and maintenance personnel.

[0136] Therefore, the above method can accurately construct a target anomaly digital twin model based on the target anomaly dynamic heterogeneous network, the target anomaly dynamic heterogeneous sub-network, the target anomaly dynamic heterogeneous local network, and / or the target anomaly network nodes; accurately predict and generate the target fault evolution path based on the target anomaly digital twin model; and accurately pre-execute target operations based on the target fault evolution path to ensure that the target resilience of the multi-level target dynamic heterogeneous network is greater than or equal to the preset resilience, thereby accurately improving the robustness and survivability of the dynamic heterogeneous network.

[0137] In some feasible implementations, the target operation may include, when the target connectivity is determined to be less than the preset connectivity, the target operation may include, the target redundant path initiation operation, the target virtual node introduction operation, and / or, the target topology reconstruction operation.

[0138] For example, if the first-level target agent determines that the target connectivity of the target dynamic heterogeneous network is less than the preset connectivity, the first-level target agent can control the execution of the corresponding target redundant path initiation operation, target virtual node introduction operation, and / or target topology reconstruction operation.

[0139] For example, if the secondary target agent determines that the target connectivity corresponding to the target dynamic heterogeneous sub-network is less than the preset connectivity, the secondary target agent can control the execution of the corresponding target redundant path initiation operation, target virtual node introduction operation, and / or target topology reconstruction operation.

[0140] For example, if the third-level target agent determines that the target connectivity of the target dynamic heterogeneous local area network is less than the preset connectivity, the third-level target agent can control the execution of the corresponding target redundant path initiation operation, target virtual node introduction operation, and / or target topology reconstruction operation.

[0141] For example, if the fourth-level target agent determines that the target connectivity of the target network node is less than the preset connectivity, the fourth-level target agent can control the execution of the corresponding target redundant path initiation operation, target virtual node introduction operation, and / or target topology reconstruction operation.

[0142] Therefore, the above method can accurately execute target redundant path initiation operation, target virtual node introduction operation, and / or target topology reconstruction operation when the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target connectivity corresponding to the target network node is less than the preset connectivity, so as to ensure that the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target resilience corresponding to the target network node is greater than or equal to the corresponding preset resilience, thereby accurately improving the robustness and survivability of multi-level target dynamic heterogeneous networks.

[0143] In some feasible implementations, the target operation may also include, when the target fault tolerance is determined to be less than the preset fault tolerance, the target operation may include: target node backup operation, target redundant link addition operation, and / or target containerized microservice startup operation.

[0144] For example, if the first-level target agent determines that the target tolerance corresponding to the target dynamic heterogeneous network is less than the preset tolerance, the first-level target agent can control the execution of the corresponding target node backup operation, target redundant link addition operation, and / or target containerized microservice startup operation.

[0145] For example, if the secondary target agent determines that the target tolerance corresponding to the target dynamic heterogeneous sub-network is less than the preset tolerance, the secondary target agent can control the execution of the corresponding target node backup operation, target redundant link addition operation, and / or target containerized microservice startup operation.

[0146] For example, if the Level 3 target agent determines that the target tolerance corresponding to the target dynamic heterogeneous local area network is less than the preset tolerance, the Level 3 target agent can control the execution of the corresponding target node backup operation, target redundant link addition operation, and / or target containerized microservice startup operation.

[0147] For example, if the Level 4 target agent determines that the target tolerance corresponding to the target network node is less than the preset tolerance, the Level 4 target agent can control the execution of the corresponding target node backup operation, target redundant link addition operation, and / or target containerized microservice startup operation.

[0148] Therefore, the above method can accurately execute target node backup operations, target redundant link addition operations, and / or target containerized microservice startup operations when the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target tolerance corresponding to the target network node is less than the preset tolerance. This ensures that the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target resilience corresponding to the target network node is greater than or equal to the corresponding preset resilience, thereby accurately improving the robustness and survivability of multi-level target dynamic heterogeneous networks.

[0149] In some feasible implementations, the target operation may also include, when it is determined that the target's resistance to attack is less than a preset resistance to attack, the target operation may include: target firewall activation operation, target attack source isolation operation, and / or target honeypot trapping operation.

[0150] For example, if the first-level target agent determines that the target attack degree corresponding to the target dynamic heterogeneous network is less than the preset attack degree, the first-level target agent can control the execution of the corresponding target firewall startup operation, target attack source isolation operation, and / or target honeypot trapping operation.

[0151] For example, if the secondary target agent determines that the target attack degree corresponding to the target dynamic heterogeneous sub-network is less than the preset attack degree, the secondary target agent can control the execution of the corresponding target firewall startup operation, target attack source isolation operation, and / or target honeypot trapping operation.

[0152] For example, if the Level 3 target agent determines that the target attack intensity corresponding to the target dynamic heterogeneous local area network is less than the preset attack intensity, the Level 3 target agent can control the execution of the corresponding target firewall startup operation, target attack source isolation operation, and / or target honeypot trapping operation.

[0153] For example, if the Level 4 target agent determines that the target attack intensity corresponding to the target network node is less than the preset attack intensity, the Level 4 target agent can control the execution of the corresponding target firewall startup operation, target attack source isolation operation, and / or target honeypot trapping operation.

[0154] Therefore, the above method can accurately execute target firewall startup, target attack source isolation, and / or target honeypot trapping operations when the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target attack resistance of the target network node is less than the preset attack resistance. This ensures that the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target survivability of the target network node is greater than or equal to the corresponding preset survivability, thereby accurately improving the robustness and survivability of multi-level target dynamic heterogeneous networks.

[0155] In some feasible implementations, the target operation may also include, when the target death degree is determined to be greater than or equal to the preset death degree, the target operation may include: target edge node construction operation, target architecture decentralization operation, and / or target load migration operation.

[0156] For example, if the first-level target agent determines that the target death degree corresponding to the target dynamic heterogeneous network is greater than or equal to the preset death degree, then the first-level target agent can control the execution of the corresponding target edge node construction operation, target architecture decentralization operation, and / or target load migration operation.

[0157] For example, if the secondary target agent determines that the target death degree corresponding to the target dynamic heterogeneous sub-network is greater than or equal to the preset death degree, the secondary target agent can control the execution of the corresponding target edge node construction operation, target architecture decentralization operation, and / or target load migration operation.

[0158] For example, if the third-level target agent determines that the target death degree corresponding to the target dynamic heterogeneous local area network is greater than or equal to the preset death degree, the third-level target agent can control the execution of the corresponding target edge node construction operation, target architecture decentralization operation, and / or target load migration operation.

[0159] For example, if the fourth-level target agent determines that the target death degree corresponding to the target network node is greater than or equal to the preset death degree, the fourth-level target agent can control the execution of the corresponding target edge node construction operation, target architecture decentralization operation, and / or target load migration operation.

[0160] Therefore, the above method can accurately execute target edge node construction operations, target architecture decentralization operations, and / or target load migration operations when the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target death degree corresponding to the target network node is greater than or equal to the preset death degree. This ensures that the target dynamic heterogeneous network, target dynamic heterogeneous sub-network, target dynamic heterogeneous local area network, and / or the target resilience corresponding to the target network node is greater than or equal to the corresponding preset resilience, thereby accurately improving the robustness and survivability of multi-level target dynamic heterogeneous networks.

[0161] In some feasible implementations, the target mortality rate is determined according to the following formula:

[0162]

[0163] in, Used to represent Time-based target dynamic heterogeneous network individuals The corresponding target mortality rate; Used to represent the first scaling factor; Used to represent individual targets in a dynamic heterogeneous network within historical target data. The benchmark risk; β Used to represent the second scaling factor; k Used to represent target threat factors; M Used to represent target threat factors k Quantity; Used to represent target threat factors k The corresponding weights; Used to represent Time-based target dynamic heterogeneous network individuals Target threat factors faced k The strength; Used to represent the third scaling factor; Used to represent individuals in a target dynamic heterogeneous network. A set of adjacent or related target-dynamic heterogeneous network individuals; Used to indicate ( t -1) Time-based target dynamic heterogeneous network individuals j The probability of death.

[0164] It should be noted that in the above formula... Used to determine the historical mortality risk baseline for individuals in a target dynamic heterogeneous network; in the above formula Used to determine the risk resulting from the superposition of multiple real-time threat factors; the above Used to determine the maximum risk from neighboring individuals or systematically related individuals at the previous moment.

[0165] It should be noted that the aforementioned target dynamic heterogeneous network individuals may include: target dynamic heterogeneous network individuals, target dynamic heterogeneous sub-network individuals, target dynamic heterogeneous local area network individuals, and / or, target network node individuals.

[0166] Therefore, based on the above formula, the method can accurately determine the historical mortality risk baseline, real-time threat superposition risk, and cascading transmission risk corresponding to the target dynamic heterogeneous network individual, the target dynamic heterogeneous sub-network individual, the target dynamic heterogeneous local area network individual, and / or the target network node individual. This improves the accuracy of determining the mortality degree of the target dynamic heterogeneous network individual, the target dynamic heterogeneous sub-network individual, the target dynamic heterogeneous local area network individual, and / or the target network node individual, thereby further improving the accuracy of determining the target resilience. When it is determined that the target resilience of the target dynamic heterogeneous network individual, the target dynamic heterogeneous sub-network individual, the target dynamic heterogeneous local area network individual, and / or the target network node individual is less than the preset resilience, the target operation can be accurately executed to make the target resilience greater than or equal to the preset resilience, thereby accurately improving the robustness and survivability of the multi-level target dynamic heterogeneous network.

[0167] Based on this, the resilience testing method based on dynamic heterogeneous networks provided in this application includes: acquiring target data based on a multi-level target smart grid; determining the target resilience based on the target data; and, if the target resilience is determined to be less than a preset resilience, performing a target operation to make the target resilience greater than or equal to the preset resilience. The target data includes: target connectivity, target fault tolerance, target attack resistance, and / or target mortality of the multi-level target dynamic heterogeneous network. This application can improve the accuracy of perception of the dynamic heterogeneous network state, thereby improving the accuracy of dynamic heterogeneous network resilience testing, and ultimately enhancing the robustness and survivability of the dynamic heterogeneous network.

[0168] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0169] The above is an introduction to the method embodiments. The following describes the solution described in this application through device embodiments.

[0170] A second aspect of this application provides a robustness testing system based on dynamic heterogeneous networks, applicable to the method described above. Figure 2 This is a structural schematic diagram of a vehicle identification system 200 based on millimeter-wave radar, provided as an embodiment of this application. Figure 2 The survivability testing system 200 based on dynamic heterogeneous networks shown includes: an acquisition unit 210, a determination unit 220, and an execution unit 230.

[0171] Acquisition unit 210 is used to acquire target data based on a multi-level target smart grid;

[0172] The determination unit 220 is used to determine the target's damage resistance based on the target data;

[0173] Execution unit 230 is used to perform target operation to make the target resilience greater than or equal to the preset resilience when it is determined that the target resilience is less than the preset resilience;

[0174] The target data includes: target connectivity, target fault tolerance, target attack resistance, and / or target death rate in a multi-level target dynamic heterogeneous network.

[0175] Figure 3 This is a schematic diagram of the structure of an electronic device 300 provided in an embodiment of this application. Figure 3 As shown, the electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage section 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the terminal device or server. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0176] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. A removable medium 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 310 as needed so that computer programs read from it can be installed into storage section 308 as needed.

[0177] Specifically, according to embodiments of this application, the above method flow steps can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a machine-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the system of this application.

[0178] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0179] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0180] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be located in a processor. The names of these units or modules do not, in certain circumstances, constitute a limitation on the unit or module itself.

[0181] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A robustness testing method based on dynamic heterogeneous networks, characterized in that, include: Target data is acquired based on a multi-level intelligent grid system. Based on the target data, determine the target's survivability; If it is determined that the target damage resistance is less than the preset damage resistance, a target operation is performed to make the target damage resistance greater than or equal to the preset damage resistance. The target data includes: target connectivity, target fault tolerance, target attack resistance, and / or target death rate of a multi-level target dynamic heterogeneous network; Among them, when the first-level target agent determines that the target resilience of the target dynamic heterogeneous network is less than the corresponding preset resilience, the target operation is controlled to make the target resilience of the target dynamic heterogeneous network greater than or equal to the corresponding preset resilience. If the secondary target agent determines that the target resilience of the target dynamic heterogeneous subnetwork is less than the corresponding preset resilience, the target operation is controlled to be executed so that the target resilience of the target dynamic heterogeneous subnetwork is greater than or equal to the corresponding preset resilience. If the third-level target agent determines that the target resilience of the target dynamic heterogeneous local area network is less than the corresponding preset resilience, the target operation is controlled to make the target resilience of the target dynamic heterogeneous local area network greater than or equal to the corresponding preset resilience. If the Level 4 target agent determines that the target resilience of the target network node is less than the corresponding preset resilience, it controls the execution of target operations to make the target resilience of the target network node greater than or equal to the corresponding preset resilience.

2. The robustness testing method based on dynamic heterogeneous networks according to claim 1, characterized in that, The method further includes: Obtain the target information corresponding to the multi-level target dynamic heterogeneous network; Based on the target information, the multi-level target smart grid is generated; The target information includes: target network topology information, target node attribute information, target service and application mapping relationship information, target security configuration information, target network operation status information, target geographical distribution information, and / or target management interface information.

3. The robustness testing method based on dynamic heterogeneous networks according to claim 2, characterized in that, The multi-level target smart grid is equipped with corresponding multi-level target intelligent agents; Among them, the first-level target smart grid is configured with a first-level target intelligent agent to regulate the target dynamic heterogeneous network; A secondary target intelligent grid is configured with a secondary target intelligent agent to regulate the dynamic heterogeneous sub-network of targets; The three-level target intelligent grid is equipped with three-level target intelligent agents to regulate the dynamic heterogeneous local area network of targets. And / or, a four-level target smart grid, setting up four-level target intelligent agents to regulate target network nodes.

4. The robustness testing method based on dynamic heterogeneous networks according to claim 3, characterized in that, The multi-level target intelligent agent is used to determine the corresponding target frequency information based on the corresponding target network operation status information; Based on the target frequency information, perform a target test operation to obtain the target data.

5. The robustness testing method based on dynamic heterogeneous networks according to claim 3, characterized in that, The step of determining the target survivability based on the target data includes: The first target resilience is determined based on the target connectivity and corresponding first weight, target fault tolerance and corresponding second weight, target attack resistance and corresponding third weight, and / or target death degree and corresponding fourth weight of the target dynamic heterogeneous network. The second target resilience is determined based on the target connectivity and corresponding first weight, target fault tolerance and corresponding second weight, target attack resistance and corresponding third weight, and / or target death degree and corresponding fourth weight of the target dynamic heterogeneous sub-network. The third target resilience is determined based on the target connectivity and corresponding first weight, target fault tolerance and corresponding second weight, target attack resistance and corresponding third weight, and / or target death degree and corresponding fourth weight of the target dynamic heterogeneous local area network. The fourth target resilience is determined based on the target network node's target connectivity and corresponding first weight, target fault tolerance and corresponding second weight, target attack resistance and corresponding third weight, and / or target death rate and corresponding fourth weight.

6. The robustness testing method based on dynamic heterogeneous networks according to claim 5, characterized in that, The third weight corresponding to the target dynamic heterogeneous network is greater than the first weight; Wherein, the first weight, the second weight, and the fourth weight are equal; The first weight and the second weight corresponding to the target dynamic heterogeneous sub-network are equal; the third weight and the fourth weight are equal; Wherein, the fourth weight is less than the first weight; The first weight corresponding to the target dynamic heterogeneous local area network is equal to the fourth weight; the second weight is equal to the third weight; Wherein, the third weight is less than the first weight; The fourth weight corresponding to the target network node is greater than the first weight; The first weight is equal to the second weight; The second weight is greater than the third weight.

7. The robustness testing method based on dynamic heterogeneous networks according to any one of claims 1 to 5, characterized in that, The step of performing a target operation to make the target damage resistance greater than or equal to the preset damage resistance when the target damage resistance is determined to be less than the preset damage resistance includes: Based on the target anomaly dynamic heterogeneous network, the target anomaly dynamic heterogeneous sub-network, the target anomaly dynamic heterogeneous local network, and / or the target anomaly network nodes, construct a target anomaly digital twin model. Based on the target anomaly digital twin model, generate the target fault evolution path; Execute the target operation according to the target fault evolution path.

8. The robustness testing method based on dynamic heterogeneous networks according to claim 7, characterized in that, Also includes: If the target connectivity is determined to be less than a preset connectivity, the target operation includes: Target redundant path initiation operation, target virtual node introduction operation, and / or, target topology reconstruction operation; If the target fault tolerance is determined to be less than the preset fault tolerance, the target operation includes: target node backup operation, target redundant link addition operation, and / or target containerized microservice startup operation. If the target's resistance to attack is determined to be less than a preset resistance to attack, the target operation includes: target firewall startup operation, target attack source isolation operation, and / or target honeypot trapping operation; If the target death level is determined to be greater than or equal to the preset death level, the target operation includes: target edge node construction operation, target architecture decentralization operation, and / or target load migration operation.

9. The robustness testing method based on dynamic heterogeneous networks according to claim 8, characterized in that, The target mortality rate is determined according to the following formula: in, Used to represent Time-based target dynamic heterogeneous network individuals The corresponding target mortality rate; Used to represent the first scaling factor; Used to represent individual targets in a dynamic heterogeneous network within historical target data. The benchmark risk; Used to represent the second scaling factor; Used to represent target threat factors; Used to represent target threat factors Quantity; Used to represent target threat factors The corresponding weights; Used to represent Time-based target dynamic heterogeneous network individuals Target threat factors faced The strength; Used to represent the third scaling factor; Used to represent individuals in a target dynamic heterogeneous network. A set of adjacent or related target-dynamic heterogeneous network individuals; Used to represent Time-based target dynamic heterogeneous network individuals The probability of death.

10. A robustness testing system based on dynamic heterogeneous networks, applicable to the method described in claim 1, characterized in that, include: The acquisition unit is used to acquire target data based on a multi-level target smart grid. A determining unit is used to determine the target's survivability based on the target data; An execution unit is configured to perform a target operation to make the target resilience greater than or equal to the preset resilience when it is determined that the target resilience is less than the preset resilience. The target data includes: target connectivity, target fault tolerance, target attack resistance, and / or target death rate of a multi-level target dynamic heterogeneous network; Among them, when the first-level target agent determines that the target resilience of the target dynamic heterogeneous network is less than the corresponding preset resilience, the target operation is controlled to make the target resilience of the target dynamic heterogeneous network greater than or equal to the corresponding preset resilience. If the secondary target agent determines that the target resilience of the target dynamic heterogeneous subnetwork is less than the corresponding preset resilience, the target operation is controlled to be executed so that the target resilience of the target dynamic heterogeneous subnetwork is greater than or equal to the corresponding preset resilience. If the third-level target agent determines that the target resilience of the target dynamic heterogeneous local area network is less than the corresponding preset resilience, the target operation is controlled to make the target resilience of the target dynamic heterogeneous local area network greater than or equal to the corresponding preset resilience. If the Level 4 target agent determines that the target resilience of the target network node is less than the corresponding preset resilience, it controls the execution of target operations to make the target resilience of the target network node greater than or equal to the corresponding preset resilience.

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