A risk assessment method and terminal based on a topology backbone autonomous system

Abstract

The application discloses a kind of risk assessment method and terminal based on topological backbone autonomous system, extract backbone part in AS network, according to the dependence degree of the shortest path of each node pair in AS network on backbone part, calculate the backbone dependence and non-backbone dependence of each shortest path, according to the backbone dependence and non-backbone dependence of each shortest path, calculate the node pair backbone dependence and node pair non-backbone dependence of each node pair on backbone part, and according to the node pair backbone dependence and node pair non-backbone dependence, calculate the core dependence degree and non-core dependence degree of each node on backbone part, determine the anti-risk ability of node according to the core dependence degree and non-core dependence degree of each node, utilize the multi-granularity information from path to node pair to node, consider more far-reaching node influence, have the ability to reflect local and global characteristics simultaneously, more accurately risk assessment to the object to be evaluated, help to select the best communication cooperation object.

Description

Technical Field

[0001] This invention relates to the field of risk assessment technology, and in particular to a risk assessment method and terminal for autonomous systems based on a topology backbone. Background Technology

[0002] Currently, based on business needs, it is necessary to conduct communication risk assessments on multiple companies and institutions to determine their risk resistance capabilities. This allows for the expansion of high-quality partners and the avoidance of risky partners based on the assessment results.

[0003] Autonomous System (AS) networks can effectively perform risk assessment. A Chinese patent (CN115604125A) proposes a method for ranking the influence of nodes in regional networks based on multiple attributes at the AS level of the Internet. Its key technical point lies in calculating the influence of a node's second-order neighbors and integrating it into the node's attributes for ranking. While this method considers the contribution of a node's neighbors' influence to its importance, it only calculates the influence of second-order neighbors, neglecting broader node influences and failing to comprehensively consider the combined contribution of various factors to a node's influence. These limitations may result in low distinguishability between nodes with similar k-core values, and the generality and distinguishability of the ranking results need further improvement, ultimately hindering accurate risk assessment.

[0004] Therefore, how to select communication partners while taking into account the far-reaching impact of nodes is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a risk assessment method and terminal for autonomous systems based on topology backbone, which can achieve risk assessment more accurately.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A risk assessment method for autonomous systems based on topology backbone, comprising the following steps:

[0008] Construct an AS network by treating each AS and the object to be evaluated as a node, and extract the backbone of the AS network.

[0009] The backbone dependency and non-backbone dependency of each shortest path are calculated based on the degree of dependency of the shortest path of each node pair in the AS network on the backbone.

[0010] Calculate the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes to the backbone part based on the backbone dependency and non-backbone dependency of each shortest path, and calculate the core dependency degree and non-core dependency degree of each node to the backbone part based on the node-to-backbone dependency and the node-to-non-backbone dependency.

[0011] The risk resistance capability of each node is determined based on its core dependency level and non-core dependency level.

[0012] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0013] A risk assessment terminal for an autonomous system based on a topology backbone includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0014] Construct an AS network by treating each AS and the object to be evaluated as a node, and extract the backbone of the AS network.

[0015] The backbone dependency and non-backbone dependency of each shortest path are calculated based on the degree of dependency of the shortest path of each node pair in the AS network on the backbone.

[0016] Calculate the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes to the backbone part based on the backbone dependency and non-backbone dependency of each shortest path, and calculate the core dependency degree and non-core dependency degree of each node to the backbone part based on the node-to-backbone dependency and the node-to-non-backbone dependency.

[0017] The risk resistance capability of each node is determined based on its core dependency level and non-core dependency level.

[0018] The beneficial effects of this invention are as follows: It extracts the backbone of the constructed AS network, calculates the backbone dependency and non-backbone dependency of each shortest path based on the dependency of each node pair on the backbone, calculates the node-to-backbone dependency and node-to-non-backbone dependency of each node pair on the backbone based on the backbone dependency and non-backbone dependency of each shortest path, calculates the core dependency and non-core dependency of each node on the backbone based on the node-to-backbone dependency and node-to-non-backbone dependency, and determines the risk resistance of each node based on the core dependency and non-core dependency of each node. It utilizes multi-granularity information from path to node pair to node, considers the far-reaching impact of nodes, and has the ability to simultaneously reflect local and global characteristics. Finally, it determines the risk resistance of each node based on the core dependency and non-core dependency of each node, enabling more accurate risk assessment of the object to be evaluated and helping to select the best communication cooperation partner. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the steps of a risk assessment method for an autonomous system based on a topology backbone, according to an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the structure of a risk assessment terminal for an autonomous system based on a topology backbone, according to an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram illustrating the extraction of the backbone component in the risk assessment method for autonomous systems based on topology backbone according to an embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram illustrating the impact of the backbone component on the shortest path and its distribution in the risk assessment method for autonomous systems based on topology backbone according to an embodiment of the present invention.

[0023] Figure 5 This is a schematic diagram of node classification in the risk assessment method for autonomous systems based on topology backbone according to an embodiment of the present invention;

[0024] Figure 6 This is a schematic diagram illustrating the core support for network communication efficiency in the risk assessment method for autonomous systems based on topology backbone according to an embodiment of the present invention. Detailed Implementation

[0025] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0026] Please refer to Figure 1 A risk assessment method for autonomous systems based on topology backbone, comprising the following steps:

[0027] Construct an AS network by treating each AS and the object to be evaluated as a node, and extract the backbone of the AS network.

[0028] The backbone dependency and non-backbone dependency of each shortest path are calculated based on the degree of dependency of the shortest path of each node pair in the AS network on the backbone.

[0029] Calculate the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes to the backbone part based on the backbone dependency and non-backbone dependency of each shortest path, and calculate the core dependency degree and non-core dependency degree of each node to the backbone part based on the node-to-backbone dependency and the node-to-non-backbone dependency.

[0030] The risk resistance capability of each node is determined based on its core dependency level and non-core dependency level.

[0031] As can be seen from the above description, the beneficial effects of the present invention are as follows: The backbone of the constructed AS network is extracted; the backbone dependency and non-backbone dependency of each shortest path are calculated based on the dependence of each node pair on the backbone; the node-to-backbone dependency and node-to-non-backbone dependency of each node pair on the backbone are calculated based on the backbone dependency and non-backbone dependency of each shortest path; the core dependency and non-core dependency of each node on the backbone are calculated based on the node-to-backbone dependency and node-to-non-backbone dependency; and the risk resistance of each node is determined based on the core dependency and non-core dependency of each node. This utilizes multi-granularity information from path to node pair to node, considering a deeper impact on nodes, and has the ability to simultaneously reflect local and global characteristics. Finally, the risk resistance of each node is determined based on its core dependency and non-core dependency, enabling more accurate risk assessment of the evaluated object and facilitating the selection of the best communication cooperation partner.

[0032] Further, calculating the backbone dependency and non-backbone dependency of each shortest path based on the dependency of each node pair in the AS network on the backbone includes:

[0033]

[0034] In the formula, Let represent the backbone dependency of the shortest path ij, n represent the total number of nodes in the shortest path ij, and m represent the total number of nodes in the shortest path that belong to the backbone. Let represent the non-backbone dependencies of the shortest path ij, and k represent the backbone nodes traversed by the shortest path ij.

[0035] As described above, the backbone dependency and non-backbone dependency of the shortest path are calculated based on the total number of nodes in the shortest path and the total number of nodes belonging to the backbone, which effectively measures the degree of dependence of the shortest path on the backbone in the network, thus reflecting the effectiveness of the backbone.

[0036] Further, the extraction of the backbone portion of the AS network includes:

[0037] The network hierarchy was determined from the AS network using the k-kernel decomposition method;

[0038] Nodes without upper-level connections are selected from the network hierarchy, and the backbone is obtained based on these nodes.

[0039] As described above, the k-kernel decomposition method can quickly find subgraph structures that meet certain close relationship conditions in the network graph, i.e., the network hierarchy structure. By selecting nodes without upper-level connections, the backbone of the AS network can be obtained.

[0040] Further, the step of calculating the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes to the backbone part based on the backbone dependency and non-backbone dependency of each shortest path, and calculating the core dependency degree and non-core dependency degree of each node to the backbone part based on the node-to-backbone dependency and the node-to-non-backbone dependency, includes:

[0041] The backbone dependencies of each shortest path are summed to obtain the node-to-backbone dependencies of each pair of nodes to the backbone part, and the non-backbone dependencies of each shortest path are summed to obtain the node-to-non-backbone dependencies of each pair of nodes to the backbone part.

[0042] The nodes are summed up according to their dependencies on the backbone to obtain the core dependency degree of each node on the backbone. The nodes are summed up according to their dependencies on the non-backbone to obtain the non-core dependency degree of each node on the backbone.

[0043] As described above, by summing the backbone dependencies and non-backbone dependencies of each shortest path, we can obtain the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes in relation to the backbone. By summing the node-to-backbone dependency and node-to-non-backbone dependency, we can obtain the core dependency and non-core dependency of each node in relation to the backbone. By summing these dependencies, we can more accurately measure the risk resistance of each node.

[0044] Furthermore, determining the risk resistance capability of a node based on its core dependency level and non-core dependency level includes:

[0045] If the core dependency level of the node is higher than a first preset threshold and the non-core dependency level is higher than a second preset threshold, then the node is determined to have high risk resistance.

[0046] Also includes:

[0047] Choose communication partners from the nodes with high risk resistance.

[0048] As described above, nodes with high core and non-core dependencies have more feasible paths, enabling them to cope with random failures of other nodes. Furthermore, connecting to the core reduces the distance between the node and other nodes in the network, thus improving communication efficiency. Therefore, such companies and organizations have balanced communication efficiency with stable operation and have a high degree of risk resistance.

[0049] Please refer to Figure 2 A risk assessment terminal for an autonomous system based on a topology backbone includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0050] Construct an AS network by treating each AS and the object to be evaluated as a node, and extract the backbone of the AS network.

[0051] The backbone dependency and non-backbone dependency of each shortest path are calculated based on the degree of dependency of the shortest path of each node pair in the AS network on the backbone.

[0052] Calculate the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes to the backbone part based on the backbone dependency and non-backbone dependency of each shortest path, and calculate the core dependency degree and non-core dependency degree of each node to the backbone part based on the node-to-backbone dependency and the node-to-non-backbone dependency.

[0053] The risk resistance capability of each node is determined based on its core dependency level and non-core dependency level.

[0054] As can be seen from the above description, the beneficial effects of the present invention are as follows: The backbone of the constructed AS network is extracted; the backbone dependency and non-backbone dependency of each shortest path are calculated based on the dependence of each node pair on the backbone; the node-to-backbone dependency and node-to-non-backbone dependency of each node pair on the backbone are calculated based on the backbone dependency and non-backbone dependency of each shortest path; the core dependency and non-core dependency of each node on the backbone are calculated based on the node-to-backbone dependency and node-to-non-backbone dependency; and the risk resistance of each node is determined based on the core dependency and non-core dependency of each node. This utilizes multi-granularity information from path to node pair to node, considering a deeper impact on nodes, and has the ability to simultaneously reflect local and global characteristics. Finally, the risk resistance of each node is determined based on its core dependency and non-core dependency, enabling more accurate risk assessment of the evaluated object and facilitating the selection of the best communication cooperation partner.

[0055] Further, calculating the backbone dependency and non-backbone dependency of each shortest path based on the dependency of each node pair in the AS network on the backbone includes:

[0056]

[0057] In the formula, Let represent the backbone dependency of the shortest path ij, n represent the total number of nodes in the shortest path ij, and m represent the total number of nodes in the shortest path that belong to the backbone. Let represent the non-backbone dependencies of the shortest path ij, and k represent the backbone nodes traversed by the shortest path ij.

[0058] As described above, the backbone dependency and non-backbone dependency of the shortest path are calculated based on the total number of nodes in the shortest path and the total number of nodes belonging to the backbone, which effectively measures the degree of dependence of the shortest path on the backbone in the network, thus reflecting the effectiveness of the backbone.

[0059] Further, the extraction of the backbone portion of the AS network includes:

[0060] The network hierarchy was determined from the AS network using the k-kernel decomposition method;

[0061] Nodes without upper-level connections are selected from the network hierarchy, and the backbone is obtained based on these nodes.

[0062] As described above, the k-kernel decomposition method can quickly find subgraph structures that meet certain close relationship conditions in the network graph, i.e., the network hierarchy structure. By selecting nodes without upper-level connections, the backbone of the AS network can be obtained.

[0063] Further, the step of calculating the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes to the backbone part based on the backbone dependency and non-backbone dependency of each shortest path, and calculating the core dependency degree and non-core dependency degree of each node to the backbone part based on the node-to-backbone dependency and the node-to-non-backbone dependency, includes:

[0064] The backbone dependencies of each shortest path are summed to obtain the node-to-backbone dependencies of each pair of nodes to the backbone part, and the non-backbone dependencies of each shortest path are summed to obtain the node-to-non-backbone dependencies of each pair of nodes to the backbone part.

[0065] The nodes are summed up according to their dependencies on the backbone to obtain the core dependency degree of each node on the backbone. The nodes are summed up according to their dependencies on the non-backbone to obtain the non-core dependency degree of each node on the backbone.

[0066] As described above, by summing the backbone dependencies and non-backbone dependencies of each shortest path, we can obtain the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes in relation to the backbone. By summing the node-to-backbone dependency and node-to-non-backbone dependency, we can obtain the core dependency and non-core dependency of each node in relation to the backbone. By summing these dependencies, we can more accurately measure the risk resistance of each node.

[0067] Furthermore, determining the risk resistance capability of a node based on its core dependency level and non-core dependency level includes:

[0068] If the core dependency level of the node is higher than a first preset threshold and the non-core dependency level is higher than a second preset threshold, then the node is determined to have high risk resistance.

[0069] Also includes:

[0070] Choose communication partners from the nodes with high risk resistance.

[0071] As described above, nodes with high core and non-core dependencies have more feasible paths, enabling them to cope with random failures of other nodes. Furthermore, connecting to the core reduces the distance between the node and other nodes in the network, thus improving communication efficiency. Therefore, such companies and organizations have balanced communication efficiency with stable operation and have a high degree of risk resistance.

[0072] The risk assessment method and terminal for autonomous systems based on topology backbone described above are applicable to scenarios requiring the selection of communication partners. The following detailed embodiments illustrate these methods:

[0073] Please refer to Figure 1 , Figures 3-6 Embodiment 1 of the present invention is as follows:

[0074] A risk assessment method for autonomous systems based on topology backbone, comprising the following steps:

[0075] S1. Construct an AS network by treating each AS to be evaluated object as a node, and extract the backbone of the AS network, specifically including S11-S13:

[0076] S11. Construct an AS network by treating each AS to be evaluated object as a node.

[0077] Specifically, each AS (Autonomous System) and its associated evaluation object are treated as a node, and an edge is generated between two nodes that have a communication relationship, thereby constructing an AS network. The evaluation objects include companies and organizations that provide internet services.

[0078] S12. Use the k-kernel decomposition method to determine the network hierarchy from the AS network.

[0079] S13. Select nodes without upper-level connections from the network hierarchy, and obtain the backbone based on the nodes without upper-level connections.

[0080] like Figure 3 As shown, Figure 3 The network on the left is the AS network, and the network on the right is the backbone of the AS network.

[0081] S2. Calculate the backbone dependency and non-backbone dependency of each shortest path based on the dependency of the shortest path of each node pair in the AS network on the backbone, specifically as follows:

[0082]

[0083] In the formula, Let represent the backbone dependency of the shortest path ij, n represent the total number of nodes in the shortest path ij, and m represent the total number of nodes in the shortest path that belong to the backbone. Let represent the non-backbone dependencies of the shortest path ij, and k represent the backbone nodes traversed by the shortest path ij.

[0084] The effectiveness of the backbone is demonstrated by the changes in the shortest path distribution. This allows for the assessment of node communication efficiency through backbone dependency and node resilience through non-backbone dependency. Figure 4As shown, L represents the shortest path, P(L) represents the distribution of the shortest path, G is the original AS network, and GB is the AS network after removing the core (i.e., the backbone). After removing the core, the distribution of the shortest path P(L) of the AS network shifts, which means that the existence of the selected core is beneficial to the communication efficiency of the network.

[0085] like Figure 6 As shown, after the core is removed from the network, the distribution of the shortest path P(L) changes, and the communication efficiency decreases. Nodes with a high degree of core dependency in this network have higher communication efficiency. After the core is removed, there are nodes whose shortest paths are not affected; these nodes are those with a high degree of non-core dependency.

[0086] S3. Calculate the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes to the backbone part based on the backbone dependency and non-backbone dependency of each shortest path, and calculate the core dependency degree and non-core dependency degree of each node to the backbone part based on the node-to-backbone dependency and the node-to-non-backbone dependency, specifically including S31-S32:

[0087] S31. Sum the backbone dependencies of each shortest path to obtain the node-to-backbone dependency of each pair of nodes on the backbone. Then, the non-backbone dependencies of each shortest path are summed to obtain the non-backbone dependencies of each node pair for the backbone part.

[0088] S32. Sum the dependencies of each node on the backbone to obtain the core dependency degree of each node on the backbone. The non-core dependencies of each node are summed to obtain the degree of non-core dependency of each node on the backbone.

[0089] By displaying the degree of non-core dependency of each node on the backbone in the graph, the nodes can be automatically classified, such as... Figure 5 As shown.

[0090] S4. Determine the risk resistance capability of each node based on its core dependency level and non-core dependency level.

[0091] Specifically, if the core dependency level of the node is higher than a first preset threshold and the non-core dependency level is higher than a second preset threshold, then the node is determined to have high risk resistance.

[0092] If a node's core dependency level is higher than the first preset threshold and its non-core dependency level is higher than the second preset threshold, it means that the node has redundant shortest paths that can cope with the situation of random failure of a certain node. Furthermore, connecting to the core can reduce the distance between the node and other nodes in the network, thereby improving communication efficiency. Therefore, such companies and institutions have balanced communication efficiency and stable operation capabilities, and have a high risk resistance.

[0093] S5. Select communication cooperation partners from the evaluation objects corresponding to the nodes with high risk resistance.

[0094] Please refer to Figure 2 Embodiment two of the present invention is as follows:

[0095] A risk assessment terminal for a topology-based autonomous system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the risk assessment method for a topology-based autonomous system in Embodiment 1.

[0096] In summary, this invention provides a risk assessment method and terminal for autonomous systems based on a topology backbone. It extracts the backbone portion of the constructed AS network, calculates the backbone dependency and non-backbone dependency of each shortest path based on the dependence of each node pair on the backbone, calculates the node-to-backbone dependency and node-to-non-backbone dependency of each node pair on the backbone based on the backbone dependency and non-backbone dependency of each shortest path, and calculates the core dependency and non-core dependency of each node on the backbone based on the node-to-backbone dependency and node-to-non-backbone dependency. Finally, it determines the risk resistance of each node based on the core dependency and non-core dependency of each node. This method utilizes multi-granularity information from path to node pair to node, considering a more profound node impact, and simultaneously reflects both local and... The ability to assess global characteristics ultimately determines a node's resilience based on its core and non-core dependencies, enabling more accurate risk assessment of the evaluated entity and facilitating the selection of optimal communication partners. Furthermore, the k-kernel decomposition method can quickly identify subgraph structures (i.e., network hierarchies) that meet certain close relationship conditions within the network graph. Selecting nodes without upper-level connections yields the backbone of the AS network. Nodes with high core and non-core dependencies indicate they possess more feasible paths, enabling them to cope with random node failures. Connecting to the core reduces the distance between the node and other nodes in the network, improving communication efficiency. Therefore, such companies and organizations balance communication efficiency with stable operation, exhibiting higher resilience.

[0097] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A risk assessment method for autonomous systems based on topological backbone, characterized in that, Including the following steps: Construct an AS network by treating each AS and the object to be evaluated as a node, and extract the backbone of the AS network. The backbone dependency and non-backbone dependency of each shortest path are calculated based on the degree of dependency of the shortest path of each node pair in the AS network on the backbone. Calculate the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes to the backbone part based on the backbone dependency and non-backbone dependency of each shortest path, and calculate the core dependency degree and non-core dependency degree of each node to the backbone part based on the node-to-backbone dependency and the node-to-non-backbone dependency. The risk resistance capability of each node is determined based on its core dependency level and non-core dependency level. The step of calculating the backbone dependency and non-backbone dependency of each shortest path based on the dependency of each node pair in the AS network on the backbone includes: ； ； In the formula, Let represent the backbone dependency of the shortest path ij, n represent the total number of nodes in the shortest path ij, and m represent the total number of nodes in the shortest path that belong to the backbone. This represents the non-backbone dependencies of the shortest path ij, and k represents the backbone nodes traversed by the shortest path ij. The determination of a node's resilience based on its core dependency level and non-core dependency level includes: If the core dependency level of the node is higher than a first preset threshold and the non-core dependency level is higher than a second preset threshold, then the node is determined to have high risk resistance. Also includes: Choose communication partners from the nodes with high risk resistance.

2. The risk assessment method for an autonomous system based on a topology backbone according to claim 1, characterized in that, The extraction of the backbone portion of the AS network includes: The network hierarchy was determined from the AS network using the k-kernel decomposition method; Nodes without upper-level connections are selected from the network hierarchy, and the backbone is obtained based on these nodes.

3. The risk assessment method for an autonomous system based on a topology backbone according to claim 1, characterized in that, The step of calculating the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes to the backbone part based on the backbone dependency and non-backbone dependency of each shortest path, and calculating the core dependency degree and non-core dependency degree of each node to the backbone part based on the node-to-backbone dependency and the node-to-non-backbone dependency, includes: The backbone dependencies of each shortest path are summed to obtain the node-to-backbone dependencies of each pair of nodes to the backbone part, and the non-backbone dependencies of each shortest path are summed to obtain the node-to-non-backbone dependencies of each pair of nodes to the backbone part. The nodes are summed up according to their dependencies on the backbone to obtain the core dependency degree of each node on the backbone. The nodes are summed up according to their dependencies on the non-backbone to obtain the non-core dependency degree of each node on the backbone.

4. A risk assessment terminal for an autonomous system based on a topology backbone, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it performs the following steps: Construct an AS network by treating each AS and the object to be evaluated as a node, and extract the backbone of the AS network. The backbone dependency and non-backbone dependency of each shortest path are calculated based on the degree of dependency of the shortest path of each node pair in the AS network on the backbone. Calculate the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes to the backbone part based on the backbone dependency and non-backbone dependency of each shortest path, and calculate the core dependency degree and non-core dependency degree of each node to the backbone part based on the node-to-backbone dependency and the node-to-non-backbone dependency. The risk resistance capability of each node is determined based on its core dependency level and non-core dependency level. The step of calculating the backbone dependency and non-backbone dependency of each shortest path based on the dependency of each node pair in the AS network on the backbone includes: ； ； In the formula, Let represent the backbone dependency of the shortest path ij, n represent the total number of nodes in the shortest path ij, and m represent the total number of nodes in the shortest path that belong to the backbone. This represents the non-backbone dependencies of the shortest path ij, and k represents the backbone nodes traversed by the shortest path ij. The determination of a node's resilience based on its core dependency level and non-core dependency level includes: If the core dependency level of the node is higher than a first preset threshold and the non-core dependency level is higher than a second preset threshold, then the node is determined to have high risk resistance. Also includes: Choose communication partners from the nodes with high risk resistance.

5. A risk assessment terminal for an autonomous system based on a topology backbone according to claim 4, characterized in that, The extraction of the backbone portion of the AS network includes: The network hierarchy was determined from the AS network using the k-kernel decomposition method; Nodes without upper-level connections are selected from the network hierarchy, and the backbone is obtained based on these nodes.

6. A risk assessment terminal for an autonomous system based on a topology backbone according to claim 4, characterized in that, The step of calculating the node-to-backbone dependency and node-to-non-backbone dependency of each pair of nodes to the backbone part based on the backbone dependency and non-backbone dependency of each shortest path, and calculating the core dependency degree and non-core dependency degree of each node to the backbone part based on the node-to-backbone dependency and the node-to-non-backbone dependency, includes: The backbone dependencies of each shortest path are summed to obtain the node-to-backbone dependencies of each pair of nodes to the backbone part, and the non-backbone dependencies of each shortest path are summed to obtain the node-to-non-backbone dependencies of each pair of nodes to the backbone part. The nodes are summed up according to their dependencies on the backbone to obtain the core dependency degree of each node on the backbone. The nodes are summed up according to their dependencies on the non-backbone to obtain the non-core dependency degree of each node on the backbone.

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