Network asset data processing method and device and storage medium

Abstract

The present disclosure provides a network asset data processing method and device and a storage medium, relates to the technical field of communication, and solves the technical problem that the accuracy of the network asset data stored in the related art cannot be guaranteed. The method comprises the following steps: acquiring asset detection data of multiple dimensions; determining a detection result of target asset data in asset detection data of each dimension of the asset detection data of multiple dimensions; the detection result is any of the following: the target asset data exists, or the target asset data does not exist; determining the accuracy of the target asset data based on the detection result of the target asset data; determining a processing mode of the target asset data based on the accuracy of the target asset data; and processing the target asset data based on the processing mode of the target asset data. The present disclosure is used in the scenario of network asset positioning.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a method, apparatus and storage medium for processing network asset data. Background Technology

[0002] Currently, in scenarios involving network asset location, common methods for acquiring network asset data mainly involve actively probing network assets and / or passively identifying them. After manually reviewing the acquired network asset data to confirm its accuracy, the data is then saved to a database.

[0003] However, the accuracy of network asset data obtained by the above methods is difficult to guarantee. Determining the accuracy of network asset data through manual review requires huge human resources costs. Therefore, how to ensure the accuracy of network asset data entering the database has become an urgent technical problem to be solved. Summary of the Invention

[0004] This disclosure provides a method, apparatus, and storage medium for processing network asset data. It solves the technical problem in related technologies where the accuracy of stored network asset data is difficult to guarantee.

[0005] To achieve the above objectives, the present disclosure adopts the following technical solution:

[0006] Firstly, a method for processing network asset data is provided. This method includes: acquiring asset detection data across multiple dimensions; the asset detection data across multiple dimensions includes at least one of the following: passive detection data, active detection data, security device detection data, and internet detection data; wherein, passive detection data is network asset data acquired through passive identification, active detection data is network asset data acquired through active detection, security device detection data is network asset data detected from security devices, and internet detection data is network asset data detected from the internet; determining the detection result of target asset data in each dimension of the asset detection data across the multiple dimensions; the detection result is any one of the following: target asset data exists, or target asset data does not exist; determining the accuracy of the target asset data based on the detection result of the target asset data; determining the processing method for the target asset data based on the accuracy of the target asset data; and processing the target asset data based on the processing method for the target asset data.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: determining the proportion of asset detection data in each dimension within multi-dimensional asset detection data; determining the preset accuracy rate of asset detection data in each dimension; and determining the accuracy of the target asset based on the proportion of asset detection data in each dimension within multi-dimensional asset detection data, the preset accuracy rate of asset detection data in each dimension, and the detection results of the target asset data in each dimension of asset detection data.

[0008] In conjunction with the first aspect mentioned above, in one possible implementation, the accuracy of the target asset is the sum of the accuracy of the target asset in each of the multiple dimensions; the accuracy of the target asset in each dimension is: the proportion of the asset detection data of the target dimension in the multi-dimensional asset detection data, the preset accuracy of the asset detection data of the target dimension, and the product of the target asset detection value of the target dimension multiplied by a preset value; wherein, when the target asset data exists in the asset data of the target dimension, the target asset detection value of the target dimension is taken as the first preset value; when the target asset data does not exist in the asset data of the target dimension, the target asset detection value of the target dimension is taken as the second preset value; the target dimension is any one of the multiple dimensions.

[0009] In conjunction with the first aspect mentioned above, in one possible implementation method, the processing method for the target asset data includes: prohibiting entry into the database; determining whether the accuracy of the target asset data is less than a first threshold; and if the accuracy of the target asset data is determined to be less than the first threshold, determining that the processing method for the target asset data is to prohibit entry into the database.

[0010] In conjunction with the first aspect mentioned above, in one possible implementation method, the processing method for the target asset data further includes: warehousing pending verification; determining whether the accuracy of the target asset data is greater than or equal to a first threshold and less than a second threshold; wherein the second threshold is greater than the first threshold; and if it is determined that the accuracy of the target asset data is greater than or equal to the first threshold and less than the second threshold, then the processing method for the target asset data is determined to be warehousing pending verification.

[0011] In conjunction with the first aspect mentioned above, in one possible implementation method, the processing method for the target asset data further includes: warehousing and archiving; determining whether the accuracy of the target asset data is greater than or equal to a second threshold; and, if the accuracy of the target asset data is determined to be greater than or equal to the second threshold, determining that the processing method for the target asset data is warehousing and archiving.

[0012] Secondly, a network asset data processing device is provided, comprising: a communication unit and a processing unit; the communication unit is used to acquire asset detection data of multiple dimensions; the asset detection data of multiple dimensions includes at least one of the following: passive detection data, active detection data, security device detection data, and Internet detection data; wherein, passive detection data is network asset data acquired through passive identification, active detection data is network asset data acquired through active detection, security device detection data is network asset data detected from security devices, and Internet detection data is network asset data detected from the Internet; the processing unit is used to determine the detection result of target asset data in each dimension of the asset detection data of the multiple dimensions; the detection result is any one of the following: target asset data exists, or target asset data does not exist; the processing unit is further used to determine the accuracy of the target asset data based on the detection result of the target asset data; the processing unit is further used to determine the processing method of the target asset data based on the accuracy of the target asset data; the processing unit is further used to process the target asset data based on the processing method of the target asset data.

[0013] In conjunction with the second aspect above, in one possible implementation, the processing unit is specifically used to: determine the proportion of asset detection data in each dimension in the multi-dimensional asset detection data; determine the preset accuracy rate of asset detection data in each dimension; and determine the accuracy of the target asset based on the proportion of asset detection data in each dimension in the multi-dimensional asset detection data, the preset accuracy rate of asset detection data in each dimension, and the detection results of the target asset data in each dimension of asset detection data.

[0014] In conjunction with the second aspect above, in one possible implementation, the accuracy of the target asset is the sum of the accuracy of the target asset in each of the multiple dimensions; the accuracy of the target asset in each dimension is: the proportion of the asset detection data of the target dimension in the multi-dimensional asset detection data, the preset accuracy of the asset detection data of the target dimension, and the product of the target asset detection value of the target dimension multiplied by a preset value; wherein, when the target asset data exists in the asset data of the target dimension, the target asset detection value of the target dimension is taken as the first preset value; when the target asset data does not exist in the asset data of the target dimension, the target asset detection value of the target dimension is taken as the second preset value; the target dimension can be any one of the multiple dimensions.

[0015] In conjunction with the second aspect above, in one possible implementation, the processing unit is specifically used to: determine whether the accuracy of the target asset data is less than a first threshold; if the accuracy of the target asset data is determined to be less than the first threshold, determine that the processing method for the target asset data is to prohibit its entry into the database.

[0016] In conjunction with the second aspect above, in one possible implementation, the processing unit is specifically used to: determine whether the accuracy of the target asset data is greater than or equal to a first threshold and less than a second threshold; wherein the second threshold is greater than the first threshold; and if it is determined that the accuracy of the target asset data is greater than or equal to the first threshold and less than the second threshold, determine that the processing method for the target asset data is to be put into storage pending verification.

[0017] In conjunction with the second aspect above, in one possible implementation, the processing unit is specifically used to: determine whether the accuracy of the target asset data is greater than or equal to a second threshold; and if the accuracy of the target asset data is determined to be greater than or equal to the second threshold, determine that the processing method for the target asset data is to archive it.

[0018] Thirdly, a network asset data processing apparatus is provided, comprising: a processor and a memory; wherein the memory is used to store computer execution instructions, and when the network asset data processing apparatus is running, the processor executes the computer execution instructions stored in the memory to cause the network asset data processing apparatus to perform the network asset data processing method as described in the first aspect above and any possible implementation thereof.

[0019] Fourthly, a computer-readable storage medium is provided, which stores instructions that, when executed by a processor of a network asset data processing apparatus, cause the network asset data processing apparatus to perform the network asset data processing method as described in the first aspect above and any possible implementation thereof.

[0020] Fifthly, a chip is provided, the chip including a processor and a communication interface, the communication interface and the processor being coupled, the processor being used to run computer programs or instructions to implement the network asset data processing method as described in the first aspect above and any possible implementation thereof.

[0021] In this disclosure, the name of the aforementioned network asset data processing device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear under other names. As long as the function of each device or functional module is similar to that of this disclosure, it falls within the scope of this disclosure and its equivalents.

[0022] These or other aspects of this disclosure will become more readily apparent in the following description.

[0023] The technical solution provided in this disclosure offers at least the following beneficial effects: The network asset data processing device in this disclosure first acquires asset detection data across multiple dimensions; and determines the detection result of the target asset data in each dimension of the asset detection data; the detection result is any one of the following: the target asset data exists, or the target asset data does not exist; based on the detection result of the target asset data, the accuracy of the target asset data is determined. Based on the accuracy of the target asset data, the processing method for the target asset data is determined; based on the processing method for the target asset data, the target asset data is processed. In this way, the network asset data processing device can determine the accuracy of the target asset data based on whether it exists in the detection data across multiple dimensions, and process it using a corresponding processing method based on the accuracy of the target asset data. This reduces the human resource cost of reviewing the target asset data and ensures the accuracy of the network asset data stored in the database. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0025] Figure 1 A schematic diagram of the structure of a network asset data processing system provided in this embodiment of the disclosure;

[0026] Figure 2 A schematic diagram of the hardware structure of a network asset data processing device provided in this embodiment of the present disclosure;

[0027] Figure 3 A flowchart illustrating a network asset data processing method provided in this embodiment of the disclosure;

[0028] Figure 4 A flowchart illustrating yet another network asset data processing method provided in this disclosure embodiment;

[0029] Figure 5 A flowchart illustrating yet another network asset data processing method provided in this disclosure embodiment;

[0030] Figure 6 A flowchart illustrating yet another network asset data processing method provided in this disclosure embodiment;

[0031] Figure 7 A flowchart illustrating yet another network asset data processing method provided in this disclosure embodiment;

[0032] Figure 8 This is a schematic diagram illustrating the process of a network asset data processing device for processing network asset data, provided in an embodiment of this disclosure.

[0033] Figure 9 This is a schematic diagram of the structure of a network asset data processing device provided in an embodiment of this disclosure. Detailed Implementation

[0034] The following description, in conjunction with the accompanying drawings, details a network asset data processing method, apparatus, and storage medium provided in the embodiments of this disclosure.

[0035] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0036] The terms “first” and “second” in this disclosure and its accompanying drawings are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a particular order of objects.

[0037] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this disclosure are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus. It should be noted that in the embodiments of this disclosure, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this disclosure should not be construed as preferred or advantageous over other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0038] In the description of this disclosure, unless otherwise stated, "multiple" means two or more.

[0039] Network assets refer to a company's digital assets such as network equipment, network information, and network applications. Network assets include: hardware devices, cloud servers, operating systems, website addresses, network ports, network certificates, network domain names, network applications, network frameworks, WeChat official accounts, mini-programs, applications, application programming interfaces, source code, etc.

[0040] Currently, in enterprise network asset location scenarios, common methods include manual inspection or passive scanning. However, manual inspection requires technical personnel to inspect and collect network assets and manually record data, which can lead to missed or incorrect asset information and is inefficient. Passive scanning, on the other hand, has a limited approach to asset identification. Obtaining asset information requires continuous monitoring of network traffic, and when there is no network traffic, the asset cannot be identified, resulting in missed asset information. Furthermore, after asset information is obtained through passive scanning, it usually requires technical personnel to review it, incurring significant manpower costs.

[0041] To address the aforementioned technical issues, this disclosure provides a method for processing network asset data. This method acquires network asset data from multiple dimensions, rationally allocates the proportion of data from each dimension based on preset accuracy levels, and determines the accuracy of the target asset data acquired from each dimension. Different processing methods are determined according to the accuracy of the target asset data. Target asset data with high accuracy can be archived without review, thereby reducing the manpower cost of reviewing target asset data and ensuring the accuracy of the archived network asset data. The method includes: a network asset data processing device first acquires asset detection data from multiple dimensions; and determines the detection result of the target asset data in each dimension of the asset detection data; the detection result is any one of the following: the target asset data exists, or the target asset data does not exist; based on the detection result of the target asset data, the accuracy of the target asset data is determined; based on the accuracy of the target asset data, a processing method for the target asset data is determined; and based on the processing method, the target asset data is processed. In this way, the network asset data processing device can determine the accuracy of the target asset data based on whether the target asset data exists in the detection data from multiple dimensions, and process it using a corresponding processing method based on the accuracy of the target asset data. This reduces the manpower cost of reviewing target asset data and ensures the accuracy of the network asset data entering the database.

[0042] In one possible implementation, the aforementioned network asset data processing method can be applied to the network asset data processing system 100. The following, in conjunction with... Figure 1 This application provides a detailed description of a network asset data processing system 100 according to an embodiment. For example... Figure 1 As shown, Figure 1 A network asset data processing system 100 is provided for embodiments of this disclosure. The system includes: a security operations center 101 and a network asset data processing device 102.

[0043] The security operations center 101 is used to collect network asset data from multiple dimensions. After the collection of network asset data from multiple dimensions is completed, the security operations center 101 sends the network asset data from multiple dimensions to the network asset data processing device 102. After receiving the network asset data from the security operations center 101, the network asset data processing device 102 determines the detection result of the target asset data in each dimension of the asset detection data; based on the detection result of the target asset data, it determines the accuracy of the target asset data; based on the accuracy of the target asset data, it determines the processing method of the target asset data; and based on the processing method of the target asset data, it processes the target asset data. Optionally, the security operations center 101 includes a network asset data acquisition module 101a, which is used to collect data.

[0044] In one possible implementation, the network asset data processing device 102 acquires asset detection data from multiple dimensions, including: passive detection data, active detection data, security device detection data, and Internet detection data.

[0045] Passive detection data is mainly obtained by scanning various network logs, identifying and labeling the built-in asset types matched in the network logs, thereby obtaining network asset data.

[0046] Active data detection involves using network connection scanning software to proactively scan port services, system devices, and business applications. This allows for in-depth analysis of network asset data and the rapid discovery of such data using various methods, including subdomain brute-force attacks, upstream and downstream interfaces, search engines, intelligent distributed web crawlers, and traffic detection.

[0047] Security device detection data is mainly obtained by scanning the connection data of internal network security devices, security logs, security event addresses, situational awareness platforms, and other internal network asset connection data.

[0048] Internet probing data primarily targets and scans a company's assets exposed on the internet to identify network asset data.

[0049] In one possible implementation, the hardware structure of the network asset data processing device 102 in the aforementioned network asset data processing system 100 includes: Figure 2 The components included in the network asset data processing apparatus 200 shown below are described in detail below. Figure 2 The hardware structure of the network asset data processing device 102 is described using the network asset data processing device 200 shown as an example. Figure 2As shown, the network asset data processing device 200 includes at least one processor 201, a communication line 202, and at least one communication interface 204, and may also include a memory 203. The processor 201, memory 203, and communication interface 204 are connected via the communication line 202.

[0050] The processor 201 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present disclosure, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0051] Communication line 202 may include a path for transmitting information between the aforementioned components.

[0052] The communication interface 204 is used to communicate with other devices or communication networks. It can use any transceiver-like device, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0053] The memory 203 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of including or storing desired program code having the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0054] In one possible design, the memory 203 can exist independently of the processor 201, meaning the memory 203 can be an external memory of the processor 201. In this case, the memory 203 can be connected to the processor 201 via a communication line 202 to store execution instructions or application code, and its execution is controlled by the processor 201 to implement the network asset data processing method provided in the following embodiments of this disclosure. In another possible design, the memory 203 can also be integrated with the processor 201, meaning the memory 203 can be an internal memory of the processor 201. For example, the memory 203 can be a cache, which can be used to temporarily store some data and instruction information.

[0055] As one possible implementation, processor 201 may include one or more CPUs, for example Figure 2 CPU0 and CPU1 in the example. Alternatively, the network asset data processing device 200 may include multiple processors, such as CPU0 and CPU1. Figure 2 The processors 201 and 207 are included. Alternatively, the network asset data processing apparatus 200 may also include an output device 205 and an input device 206.

[0056] The following provides a detailed description of the network asset data processing method provided in the embodiments of this disclosure.

[0057] Figure 3 The network asset data processing method provided in the embodiments of this disclosure can be applied to, for example... Figure 2 In the network asset data processing device shown, such as Figure 3 As shown, the method includes the following S301-S305, which will be described in detail below.

[0058] S301, The network asset data processing device acquires asset detection data from multiple dimensions.

[0059] The asset detection data, which spans multiple dimensions, includes at least one of the following: passive detection data, active detection data, security device detection data, and internet detection data.

[0060] Among them, passive detection data refers to network asset data obtained through passive identification, active detection data refers to network asset data obtained through active detection, security device detection data refers to network asset data detected from security devices, and Internet detection data refers to network asset data detected from the Internet.

[0061] In one possible implementation, the network asset data processing device can obtain asset detection data from multiple dimensions from the security operations center.

[0062] S302, The network asset data processing device determines the detection results of the target asset data in each dimension of the asset detection data in multiple dimensions of asset detection data.

[0063] The detection result is either one of the following: target asset data exists, or target asset data does not exist.

[0064] In one possible implementation, the network asset data processing device can determine whether target asset data has been detected in the above four dimensions based on passive detection data, active detection data, security device detection data, and Internet detection data.

[0065] S303. The network asset data processing device determines the accuracy of the target asset data based on the detection results of the target asset data.

[0066] In one possible implementation, the network asset data processing device can determine the accuracy of the target asset data based on whether the target asset data is included in the asset detection data, which includes passive detection data, active detection data, security device detection data, and Internet detection data.

[0067] S304. The network asset data processing device determines the processing method for the target asset data based on the accuracy of the target asset data.

[0068] In one possible implementation, the network asset data processing device is pre-configured with different asset data processing methods, each of which has a corresponding accuracy range for the target asset data. Based on the accuracy of the target asset data, the network asset data processing device can determine the processing method for the target asset data.

[0069] S305. The network asset data processing device processes the target asset data based on the processing method of the target asset data.

[0070] The technical solution provided by the above embodiments can bring at least the following beneficial effects: The network asset data processing device first acquires asset detection data from multiple dimensions; and determines the detection result of the target asset data in each dimension of the asset detection data; the detection result is any one of the following: the target asset data exists, or the target asset data does not exist; based on the detection result of the target asset data, the accuracy of the target asset data is determined. Based on the accuracy of the target asset data, the processing method for the target asset data is determined; based on the processing method for the target asset data, the target asset data is processed. In this way, the network asset data processing device can determine the accuracy of the target asset data based on whether the target asset data exists in the detection data from multiple dimensions, and process it using a corresponding processing method based on the accuracy of the target asset data. This reduces the human resource cost of reviewing the target asset data and ensures the accuracy of the network asset data entering the database.

[0071] One possible way to achieve this is by combining Figure 3 ,like Figure 4 As shown, the process by which the network asset data processing device determines the accuracy of the target asset data based on the detection results of the target asset data in S303 above can be specifically implemented through the following S401-S403, which will be explained in detail below.

[0072] S401, The network asset data processing device determines the proportion of asset detection data in each dimension in the multi-dimensional asset detection data.

[0073] For example, the passive detection data acquired by the network asset data processing device accounts for 40% of the multi-dimensional asset detection data, the active detection data accounts for 40% of the multi-dimensional asset detection data, the security device detection data accounts for 15% of the multi-dimensional asset detection data, and the Internet detection data accounts for 5% of the multi-dimensional asset detection data.

[0074] S402, The network asset data processing device determines the preset accuracy rate of asset detection data for each dimension.

[0075] For example, since the same device may have multiple interfaces, it may be passively identified as multiple assets. The network asset data processing device determines that the preset accuracy of the passive detection data is 85%.

[0076] Due to a 10% error rate in the network connection scanning software, the network asset data processing device sets the preset accuracy rate of the active detection data at 90%.

[0077] Because the accuracy of the data source for the intranet security equipment connection data has a certain margin of error, the network asset data processing device determines that the preset accuracy rate of the security equipment detection data is 80%.

[0078] Because the attributes of internet assets are difficult to identify, the accuracy of internet asset data is low. The network asset data processing device sets the preset accuracy rate of internet detection data at 70%.

[0079] S403. The network asset data processing device determines the accuracy of the target asset based on the proportion of asset detection data in each dimension in the multi-dimensional asset detection data, the preset accuracy rate of asset detection data in each dimension, and the detection results of the target asset data in each dimension of asset detection data.

[0080] In one possible implementation, the accuracy of the target asset is the sum of the accuracy of the target asset in each of the multiple dimensions.

[0081] The accuracy of the target asset in each dimension is: the proportion of the target dimension's asset detection data in the multi-dimensional asset detection data, the preset accuracy of the target dimension's asset detection data, and the product of the target dimension's target asset detection value and the preset value.

[0082] Specifically, when target asset data exists in the asset data of the target dimension, the target asset detection value of the target dimension is set to the first preset value; when target asset data does not exist in the asset data of the target dimension, the target asset detection value of the target dimension is set to the second preset value; the target dimension can be any dimension among multiple dimensions.

[0083] For example, the first preset value is 0, and the second preset value is 1. That is to say, when the target asset data exists in the asset data of the target dimension, the target asset detection value of the target dimension is 1; when the target asset data does not exist in the asset data of the target dimension, the target asset detection value of the target dimension is 0; the target dimension can be any of multiple dimensions.

[0084] For example, when the network asset data processing device determines that the target asset is network address 19*.16*.*.1, the accuracy of determining network address 19*.16*.*.1 in the target asset, based on the target asset detection results in Table 1 below, is (0.4*0.85*1*10)+(0.15*0.8*1*10)+0+0=4.6; when the network asset data processing device determines that the target asset is network address 19*.16*.*.2, the accuracy of determining network address 19*.16*.*.2 in the target asset, based on the target asset detection results in Table 1 below, is (0.4*0.85*1*10)+0+0=4.6. The accuracy of address 19*.16*.*.2 is (0.4*0.85*1*10)+(0.4*0.9*1*10)+(0.15*0.8*1*10)+0=8.2; when the network asset data processing device determines that the target asset is network address 19*.16*.*.3, according to the target asset detection results in Table 1 below, the accuracy of network address 19*.16*.*.3 in the target asset can be determined as (0.15*0.8*1*10)+(0.05*0.7*1*10)+0+0=1.55.

[0085]

[0086] The technical solution provided by the above embodiments can bring at least the following beneficial effects: The network asset data processing device determines the proportion of asset detection data in each dimension of multi-dimensional asset detection data, the preset accuracy rate of asset detection data in each dimension, and the detection results of target asset data in each dimension of asset detection data. Based on the proportion of asset detection data in each dimension of multi-dimensional asset detection data, the preset accuracy rate of asset detection data in each dimension, and the detection results of target asset data in each dimension of asset detection data, the accuracy of the target asset is determined. This ensures the accuracy of the target asset data.

[0087] One possible approach involves processing the target asset data, including: archiving and storage; and combining... Figure 4 ,like Figure 5 As shown, the process by which the network asset data processing device determines the processing method of the target asset data based on the accuracy of the target asset data in S304 can be specifically implemented through the following S501-S502, which will be explained in detail below.

[0088] S501, The network asset data processing device determines whether the accuracy of the target asset data is less than a first threshold.

[0089] For example, the first threshold for the accuracy of asset data is 4, and the network asset data processing device determines whether the accuracy of the target asset data is less than 4.

[0090] S502. If the accuracy of the target asset data is less than the first threshold, the network asset data processing device determines that the processing method for the target asset data is to prohibit its entry into the warehouse and return it to be verified.

[0091] Understandably, when the accuracy of the target asset data is less than the first threshold, it indicates that the accuracy of the target asset data is low. At this time, the target asset data is prohibited from being added to the database and is awaited to be manually verified to see if the target asset data exists.

[0092] For example, the network asset data processing device determines that the target asset is network address 19*.16*.*.3, and the accuracy of the target asset is 1.55. At this time, the network asset data processing device determines that the accuracy of the target asset data is less than 4, and the network asset data processing device determines that the processing method for the target asset data is to prohibit entry into the database.

[0093] The technical solution provided by the above embodiments can bring at least the following beneficial effects: the network asset data processing device determines whether the accuracy of the target asset data is less than a first threshold. If the accuracy of the target asset data is determined to be less than the first threshold, the processing method for the target asset data is determined to be data entry and archiving. That is, if the accuracy of the target asset data is determined to be low, data entry is prohibited, and manual verification of the existence of the target asset data is required. This ensures the accuracy of the network asset data.

[0094] One possible approach to processing target asset data includes: data entry pending verification; and combining... Figure 4 ,like Figure 6 As shown, the process by which the network asset data processing device determines the processing method of the target asset data based on the accuracy of the target asset data in S304 can also be implemented through the following S601-S602, which will be explained in detail below.

[0095] S601, The network asset data processing device determines whether the accuracy of the target asset data is greater than or equal to a first threshold and less than a second threshold.

[0096] The second threshold is greater than the first threshold.

[0097] For example, the first threshold for the accuracy of asset data is 4, the second threshold is 7, and the network asset data processing device determines that the accuracy of the target asset data is greater than or equal to 4 and less than 7.

[0098] S602. When the network asset data processing device determines that the accuracy of the target asset data is greater than or equal to the first threshold and less than the second threshold, the processing method for the target asset data is to be stored in the warehouse pending verification.

[0099] Understandably, when the accuracy of the target asset data is greater than or equal to the first threshold and less than the second threshold, it indicates that the accuracy of the target asset data is relatively high. In this case, the target asset data is entered into the database and archived after being manually verified to ensure its existence.

[0100] For example, the network asset data processing device determines that the target asset is the network address 19*.16*.*.1 and the accuracy of the target asset is 4.6. At this time, the network asset data processing device determines that the accuracy of the target asset data is greater than 4 and less than 7, and the network asset data processing device determines that the processing method of the target asset data is to put it into the warehouse for verification.

[0101] The technical solution provided by the above embodiments can bring at least the following beneficial effects: When the network asset data processing device determines that the accuracy of the target asset data is greater than or equal to a first threshold and less than a second threshold, it determines that the processing method for the target asset data is to store it in the database pending verification. That is, if the accuracy of the target asset data is determined to be high, the target asset data can be stored in the database, and after manual verification of the existence of the target asset data, it is archived. This ensures the accuracy of the network asset data.

[0102] One possible approach to processing target asset data includes: archiving and storage; and combining... Figure 4 ,like Figure 7 As shown, the process by which the network asset data processing device determines the processing method of the target asset data based on the accuracy of the target asset data in S304 can also be implemented through the following S701-S702, which will be explained in detail below.

[0103] S701, The network asset data processing device determines whether the accuracy of the target asset data is greater than or equal to a second threshold.

[0104] For example, the second threshold for the accuracy of asset data is 7, and the network asset data processing device determines that the accuracy of the target asset data is greater than or equal to 7.

[0105] S702. When the accuracy of the target asset data is determined to be greater than or equal to the second threshold, the network asset data processing device determines that the processing method of the target asset data is to archive it.

[0106] Understandably, when the accuracy of the target asset data is greater than or equal to the second threshold, it indicates that the accuracy of the target asset data is extremely high. In this case, the target asset data can be directly entered into the database and archived without the need for manual verification of the existence of the target asset data.

[0107] For example, the network asset data processing device determines that the target asset is the network address 19*.16*.*.2, and the accuracy of the target asset is 8.2. At this time, the network asset data processing device determines that the accuracy of the target asset data is greater than 7, and the network asset data processing device determines that the processing method of the target asset data is to store and archive it.

[0108] The technical solution provided by the above embodiments can bring at least the following beneficial effects: When the network asset data processing device determines that the accuracy of the target asset data is greater than or equal to a second threshold, it determines that the processing method for the target asset data is to archive it. That is, when the accuracy of the target asset data is determined to be extremely high, the target asset data can be directly archived without manual verification of whether the target asset data exists. This reduces the human cost of reviewing the target asset data and ensures the accuracy of the network asset data.

[0109] In one possible implementation, the process of the network asset data processing device processing network asset data includes steps 1-6, which are described below in conjunction with... Figure 8 The process of how the network asset data processing device processes network asset data is explained below:

[0110] Step 1. The network asset data processing device acquires passive detection data, active detection data, security device detection data, and Internet detection data.

[0111] Step 2. The network asset data processing device determines the detection results of the target asset.

[0112] Step 3. The network asset data processing device determines the accuracy of the target asset.

[0113] Step 4. The network asset data processing device determines whether the accuracy of the target asset is greater than or equal to the second threshold 7. If the accuracy of the target asset is greater than or equal to the second threshold 7, the target asset is determined to be archived. If not, proceed to step 5.

[0114] Step 5. The network asset data processing device determines whether the accuracy of the target asset is greater than or equal to the first threshold 4. If the accuracy of the target asset is greater than or equal to the first threshold 4 but less than the second threshold 7, the target asset is determined to be stored in the warehouse for manual verification of its accuracy. After the existence of the target asset is verified, it is stored in the warehouse and archived. If the target asset does not exist, the target asset is ignored.

[0115] Step 6. If the accuracy of the target asset is less than the first threshold 4, the target asset is prohibited from being put into storage. The accuracy of the target asset is directly verified manually. After the existence of the target asset is verified, it is put into storage and archived. If the target asset does not exist, the target asset is ignored.

[0116] As can be seen, the above mainly describes the technical solutions provided by the embodiments of this disclosure from a methodological perspective. To achieve the above functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0117] This disclosure embodiment can divide the network asset data processing device into functional modules according to the above method example. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this disclosure embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0118] like Figure 9 As shown, Figure 9 This is a schematic diagram of the structure of a network asset data processing device 900 provided in an embodiment of the present disclosure.

[0119] The network asset data processing device 900 includes: a communication unit 901 and a processing unit 902; the communication unit 901 is used to acquire asset detection data in multiple dimensions; the asset detection data in multiple dimensions includes at least one of the following: passive detection data, active detection data, security device detection data, and Internet detection data; wherein, passive detection data is network asset data acquired through passive identification, active detection data is network asset data acquired through active detection, security device detection data is network asset data detected from security devices, and Internet detection data is network asset data detected from the Internet; the processing unit 902 is used to determine the detection result of the target asset data in each dimension of the asset detection data in the multiple dimensions; the detection result is any one of the following: the target asset data exists, or the target asset data does not exist; the processing unit 902 is also used to determine the accuracy of the target asset data based on the detection result of the target asset data; the processing unit 902 is also used to determine the processing method of the target asset data based on the accuracy of the target asset data; the processing unit 902 is also used to process the target asset data based on the processing method of the target asset data.

[0120] In one possible implementation, the processing unit 902 is specifically used to: determine the proportion of asset detection data in each dimension in multi-dimensional asset detection data; determine the preset accuracy rate of asset detection data in each dimension; and determine the accuracy of the target asset based on the proportion of asset detection data in each dimension in multi-dimensional asset detection data, the preset accuracy rate of asset detection data in each dimension, and the detection results of target asset data in each dimension of asset detection data.

[0121] In one possible implementation, the accuracy of the target asset is the sum of the accuracy of the target asset in each of the multiple dimensions; the accuracy of the target asset in each dimension is: the proportion of the target dimension's asset detection data in the multi-dimensional asset detection data, the preset accuracy of the target dimension's asset detection data, and the product of the target dimension's target asset detection value multiplied by a preset value; wherein, when the target asset data exists in the target dimension's asset data, the target asset detection value of the target dimension is taken as a first preset value; when the target asset data does not exist in the target dimension's asset data, the target asset detection value of the target dimension is taken as a second preset value; the target dimension can be any one of the multiple dimensions.

[0122] In one possible implementation, the processing unit 902 is specifically used to: determine whether the accuracy of the target asset data is less than a first threshold; if the accuracy of the target asset data is determined to be less than the first threshold, determine that the processing method for the target asset data is to prohibit its entry into the database.

[0123] In one possible implementation, the processing unit 902 is specifically used to: determine whether the accuracy of the target asset data is greater than or equal to a first threshold and less than a second threshold; wherein the second threshold is greater than the first threshold; and if the accuracy of the target asset data is determined to be greater than or equal to the first threshold and less than the second threshold, determine that the processing method of the target asset data is to be put into storage pending verification.

[0124] In one possible implementation, the processing unit 902 is specifically used to: determine whether the accuracy of the target asset data is greater than or equal to a second threshold; and if the accuracy of the target asset data is determined to be greater than or equal to the second threshold, determine that the processing method for the target asset data is to archive it.

[0125] This disclosure also provides a network asset data processing apparatus, which includes a processor and a memory; wherein the memory is used to store computer execution instructions, and when the network asset data processing apparatus is running, the processor executes the computer execution instructions stored in the memory, so that the network asset data processing apparatus performs the network asset data processing method described in this disclosure.

[0126] Embodiments of this disclosure provide a computer program product containing instructions that, when executed on a computer, cause the computer to perform the network asset data processing method described in the above method embodiments.

[0127] Embodiments of this disclosure provide a chip including a processor and a communication interface, the communication interface and the processor being coupled together, the processor being used to run computer programs or instructions to implement the network asset data processing method as described in the above method embodiments.

[0128] The computer-readable storage medium may 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 computer-readable storage media (a non-exhaustive list) include: 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), registers, hard disks, optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing, or any other form of computer-readable storage medium in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In this embodiment of the disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0129] Since the apparatus, devices, computer-readable storage media, and computer program products in the embodiments of this disclosure can be applied to the above methods, the technical effects they can achieve can also be referred to the above method embodiments. The embodiments of this disclosure will not be repeated here.

[0130] The above descriptions are merely specific embodiments of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions within the technical scope disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for processing network asset data, characterized in that, include: Acquire asset detection data from multiple dimensions; The asset detection data across multiple dimensions includes: passive detection data, active detection data, security device detection data, and internet detection data; wherein, the passive detection data is network asset data obtained through passive identification, the active detection data is network asset data obtained through active detection, the security device detection data is network asset data detected from security devices, and the internet detection data is network asset data detected from the internet; Determine the detection result of the target asset data in each dimension of the asset detection data across the multiple dimensions; the detection result is any one of the following: the target asset data exists, or the target asset data does not exist; Determine the proportion of asset detection data in each dimension within the multi-dimensional asset detection data; Determine the preset accuracy rate of the asset detection data for each dimension; The accuracy of the target asset data is determined based on the proportion of asset detection data in each dimension in the multi-dimensional asset detection data, the preset accuracy rate of asset detection data in each dimension, and the detection results of target asset data in asset detection data in each dimension. Based on the accuracy of the target asset data, determine the processing method for the target asset data; The target asset data is processed based on the processing method described above.

2. The method according to claim 1, characterized in that, The accuracy of the target asset is the sum of the accuracy of the target asset in each of the multiple dimensions. The accuracy of the target asset in each dimension is: the proportion of the target dimension's asset detection data in the multi-dimensional asset detection data, the preset accuracy of the target dimension's asset detection data, and the product of the target dimension's target asset detection value and the preset value. Wherein, when the target asset data exists in the asset data of the target dimension, the target asset detection value of the target dimension is a first preset value; when the target asset data does not exist in the asset data of the target dimension, the target asset detection value of the target dimension is a second preset value; the target dimension is any one of the multiple dimensions.

3. The method according to any one of claims 1-2, characterized in that, The processing methods for the target asset data include: prohibiting its entry into the database; The step of determining the processing method for the target asset data based on the accuracy of the target asset data includes: Determine whether the accuracy of the target asset data is less than a first threshold; If the accuracy of the target asset data is determined to be less than a first threshold, the processing method for the target asset data is to prohibit its entry into the database.

4. The method according to claim 3, characterized in that, The processing method for the target asset data also includes: warehousing pending verification; The step of determining the processing method for the target asset data based on the accuracy of the target asset data includes: Determine whether the accuracy of the target asset data is greater than or equal to a first threshold and less than a second threshold; wherein the second threshold is greater than the first threshold; If the accuracy of the target asset data is determined to be greater than or equal to the first threshold and less than the second threshold, the processing method for the target asset data is determined to be warehousing pending verification.

5. The method according to claim 4, characterized in that, The processing methods for the target asset data also include: data entry and archiving; The step of determining the processing method for the target asset data based on the accuracy of the target asset data includes: Determine whether the accuracy of the target asset data is greater than or equal to the second threshold; If the accuracy of the target asset data is determined to be greater than or equal to the second threshold, the processing method for the target asset data is determined to be storage and archiving.

6. A network asset data processing device, characterized in that, include: Communication unit and processing unit; The communication unit is used to acquire asset detection data from multiple dimensions; The asset detection data across multiple dimensions includes: passive detection data, active detection data, security device detection data, and internet detection data; wherein, the passive detection data is network asset data obtained through passive identification, the active detection data is network asset data obtained through active detection, the security device detection data is network asset data detected from security devices, and the internet detection data is network asset data detected from the internet; The processing unit is configured to determine the detection result of the target asset data in each dimension of the asset detection data of the multiple dimensions; the detection result is any one of the following: the target asset data exists, or the target asset data does not exist; The processing unit is further configured to: Determine the proportion of asset detection data in each dimension within the multi-dimensional asset detection data; Determine the preset accuracy rate of the asset detection data for each dimension; The accuracy of the target asset data is determined based on the proportion of asset detection data in each dimension in the multi-dimensional asset detection data, the preset accuracy rate of asset detection data in each dimension, and the detection results of target asset data in asset detection data in each dimension. The processing unit is also used to determine the processing method of the target asset data based on the accuracy of the target asset data; The processing unit is also used to process the target asset data based on the processing method of the target asset data.

7. A network asset data processing device, characterized in that, include: A processor and a memory; wherein the memory is used to store computer execution instructions, and when the network asset data processing device is running, the processor executes the computer execution instructions stored in the memory to cause the network asset data processing device to perform the network asset data processing method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by the processor of the network asset data processing device, cause the network asset data processing device to perform the network asset data processing method according to any one of claims 1-5.

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