CN117557801A

CN117557801A - Mapping method and device of network asset, storage medium and electronic equipment

Info

Publication number: CN117557801A
Application number: CN202311500251.1A
Authority: CN
Inventors: 王善标; 常力元; 王德威; 许红梅; 满方辉; 汪志勇
Original assignee: Tianyi Safety Technology Co Ltd
Current assignee: Tianyi Safety Technology Co Ltd
Priority date: 2023-11-13
Filing date: 2023-11-13
Publication date: 2024-02-13

Abstract

The application discloses a mapping method, a mapping device, a storage medium and electronic equipment of network assets, and relates to the technical field of computers. The method comprises the steps of acquiring site service information of a target site and multimedia information carried by the target site, wherein the multimedia information at least comprises text information and image information, extracting target entity information of a target entity to which the target site belongs from the text information by equipment, extracting external application information of an external application associated with the target site from the image information, and associating the extracted entity object information, the external application information and the acquired site service information into network asset information of the target site so as to acquire a corresponding target mapping result, thereby realizing multi-dimensional mining of the network asset information of the target site and improving accuracy and comprehensiveness of network asset mapping.

Description

Mapping method and device of network asset, storage medium and electronic equipment

Technical Field

The present application relates to the field of internet technologies, and in particular, to a method and apparatus for mapping a network asset, a storage medium, and an electronic device.

Background

In recent years, with the rapid development of the Internet, a mapping mode of network assets (such as network asset information of a target site, etc.) is derived.

In the related art, an IP address of a target site is generally requested from the target site, and the received IP address is used as network asset information of the target site, so as to obtain a corresponding target mapping result.

However, the above method only considers basic site information such as an IP address, and the like, and has the problem of single mapping dimension, and based on the mapping result obtained by mapping in the method, abundant and comprehensive effective information is difficult to provide for the processing of the subsequent network asset association analysis, so that the processing accuracy of the subsequent network asset association analysis is affected.

Disclosure of Invention

The application provides a method, a device, a storage medium and electronic equipment for mapping network assets, which are used for mining multi-dimensional network asset information and improving the accuracy and the comprehensiveness of network asset mapping.

In a first aspect, the present application provides a method of mapping a network asset, the method comprising:

Receiving at least one response message sent by a target site based on a network asset request; wherein the at least one response message includes: the system comprises site service information of the target site and multimedia information carried by the target site, wherein the multimedia information at least comprises text information and image information;

extracting target entity information of a target entity to which the target site belongs from the text information; the method comprises the steps of,

extracting external application information of an external application associated with the target site from the image information;

and associating the entity object information, the external application information and the site service information into network asset information of the target site so as to obtain a corresponding target mapping result.

In a second aspect, the present application provides a mapping apparatus for a network asset, the apparatus comprising:

a receiving unit, configured to receive at least one response message sent by a target site based on a network asset request; wherein the at least one response message includes: the system comprises site service information of the target site and multimedia information carried by the target site, wherein the multimedia information at least comprises text information and image information;

The extraction unit is used for extracting target entity information of a target entity to which the target site belongs from the text information; extracting external application information of an external application associated with the target site from the image information;

and the obtaining unit is used for associating the entity object information, the external application information and the site service information into the network asset information of the target site so as to obtain a corresponding target mapping result.

Optionally, the extracting unit is configured to extract, from the image information, external application information of an external application associated with the target site, specifically configured to:

selecting at least one target image belonging to a target category from the image information based on a preset target classification index; wherein, the target classification index characterizes: a classification scheme for the target class;

for the at least one target image, the following operations are respectively executed: extracting at least one target sub-graph represented by a designated graph from one target image; wherein each target subgraph: is generated according to the geometric figure rule indicated by the preset information coding mode;

And respectively carrying out image coding analysis processing on each extracted target subgraph based on the external application associated with each target subgraph to obtain the external application information of the target site.

Optionally, if the image information includes text information, the extracting unit is further configured to:

converting the text information in the image information into text representation by adopting an optical character recognition mode, and obtaining text representation in the text information;

and extracting entity description information of the associated entity of the target site from the text representation, and associating the entity description information into network asset information of the target site.

Optionally, the multimedia information further includes file information, where the file information includes at least one document file;

the extraction unit is further configured to:

for the at least one document file, the following operations are performed respectively: acquiring document description information of one document file, extracting document content information of the one document file, and acquiring corresponding document file information;

and associating the document file information corresponding to each of the at least one document file as the network asset information of the target site.

Optionally, the multimedia information further comprises audio information, the audio information comprising at least one audio file;

the extraction unit is further configured to:

for the at least one audio file, performing the following operations respectively: acquiring audio description information of one audio file, extracting audio content information of the one audio file, and acquiring corresponding audio file information;

and associating the audio file information corresponding to each at least one audio file as network asset information of the target site.

Optionally, the multimedia information further includes video information, the video information including at least one video file, each video file including: a plurality of consecutive image frames;

the extraction unit is further configured to:

for the at least one video file, performing the following operations respectively: acquiring video description information of one video file, extracting video content information of a head image frame and a tail image frame aiming at the one video file, and acquiring corresponding video file information;

and associating the video file information corresponding to each of the at least one video file as the network asset information of the target site.

Optionally, the one video file further includes: audio frames corresponding to the plurality of image frames;

the extraction unit is further configured to:

performing voice recognition processing on each audio frame in the video file by adopting a voice recognition mode to obtain corresponding voice text representation;

extracting entity description information of an associated entity of the target site from the voice text representation, and associating the entity description information into network asset information of the target site;

the one video file further includes: subtitle information corresponding to the plurality of image frames;

the extraction unit is further configured to:

performing subtitle extraction processing on each subtitle information in the video file by adopting a subtitle extraction technology to obtain a corresponding subtitle text representation;

and extracting entity description information of the associated entity of the target site from the caption text representation, and associating the entity description information into network asset information of the target site.

In a third aspect, the present application provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of mapping any one of the network assets of the first aspect when executing the computer program.

In a fourth aspect, the present application provides a computer storage medium having stored therein computer program instructions for execution by a processor of a method of mapping any one of the network assets of the first aspect described above.

In a fifth aspect, embodiments of the present application provide a computer program product comprising computer program instructions which, when executed by a processor, implement a method of mapping any one of the network assets described in the first aspect.

The beneficial effects of the application are as follows:

in the embodiment of the application, a method, a device, equipment and a storage medium for mapping network assets are provided, which are used for mining multi-dimensional network asset information and improving the accuracy and the comprehensiveness of network asset mapping.

In the method for mapping a network asset provided in the embodiment of the present application, the device receives at least one response message sent by a target site based on a network asset request, where the response message includes: the system comprises site service information of the target site and multimedia information carried by the target site, wherein the multimedia information at least comprises text information and image information. As can be easily understood, the embodiment of the application focuses not only on the site service information of the target site, but also on the multimedia information borne by the target site, especially the text information and the image information borne by the target site, so that the network asset information of the target site can be more accurately and comprehensively reflected by mapping the text information and the image information; for example, in view of information security, when network asset association analysis is performed based on mapping network asset information, site service information of a target site can be analyzed to trace network security, and analysis can be performed by combining image-text information of the target site, so that accurate and rich analysis results are obtained, and management efficiency of corresponding network asset information is improved.

Then, extracting target entity information of a target entity to which the target site belongs from the text information, extracting external application information of an external application associated with the target site from the image information, and associating the extracted entity object information, the external application information and the obtained site service information into network asset information of the target site to obtain a corresponding target mapping result. On one hand, aiming at text information carried on a target site, a target entity to which the target site belongs is excavated from the text information, so that the follow-up information safety consideration is facilitated, and corresponding target entity information can be directly obtained based on a target mapping result; on the other hand, aiming at the image information carried on the target site, the external application related to the target site is mined from the image information, so that the follow-up information safety consideration is facilitated, and the corresponding external application information can be directly obtained based on the target mapping result; thus, the target mapping result obtained by the embodiment of the application not only can reflect the site service information of the target site, but also can reflect the site content information of the target site, such as target entity information, external application information and the like, and multi-dimensional mapping of corresponding network asset information is realized, so that the processing efficiency and accuracy of subsequent network asset association analysis are effectively improved.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:

fig. 1 is a schematic diagram of an optional application scenario in an embodiment of the present application;

FIG. 2 is a flow chart of a method for mapping network assets provided in an embodiment of the present application;

fig. 3 is a schematic diagram of a process of acquiring site service information in an embodiment of the present application;

fig. 4 is a schematic diagram of a process for obtaining target entity information in an embodiment of the present application;

fig. 5 is a schematic diagram of a process for extracting image information in an embodiment of the present application;

FIG. 6 is a schematic diagram of a process for obtaining document file information according to an embodiment of the present application;

FIG. 7 is a schematic diagram of a process for obtaining audio file information according to an embodiment of the present application;

Fig. 8 is a schematic diagram of a process for extracting video information according to an embodiment of the present application;

FIG. 9 is a schematic diagram of one possible target mapping result in an embodiment of the present application;

FIG. 10 is a schematic structural diagram of a mapping apparatus for network assets provided in an embodiment of the present application;

fig. 11 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

In the embodiment of the application, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.

The embodiment of the application relates to an artificial intelligence technology, in particular to a computer vision technology, a voice technology, a natural language processing technology and a machine learning technology in the artificial intelligence technology.

Artificial intelligence (Artificial Intelligence, AI), is a theory, method, technique, and application system that simulates, extends, and extends human intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, obtains knowledge, and uses the knowledge to obtain optimal results. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision.

The artificial intelligence technology is a comprehensive subject, and relates to the technology with wide fields, namely the technology with a hardware level and the technology with a software level. Artificial intelligence infrastructure technologies generally include, for example, sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, pre-training model technologies, operation/interaction systems, mechatronics, and the like. The pre-training model is also called a large model and a basic model, and can be widely applied to all large-direction downstream tasks of artificial intelligence after fine adjustment. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

Computer Vision (CV) is a science of studying how to "look" a machine, and more specifically, it means to replace human eyes with a camera and a Computer to perform machine Vision such as recognition, tracking and measurement on a target, and further perform graphic processing to make the Computer process into an image more suitable for human eyes to observe or transmit to an instrument for detection. As a scientific discipline, computer vision research-related theory and technology has attempted to build artificial intelligence systems that can acquire information from images or multidimensional data. The large model technology brings important innovation for the development of computer vision technology, and a pre-trained model in the vision fields of swin-transformer, viT, V-MOE, MAE and the like can be rapidly and widely applied to downstream specific tasks through fine tuning. Computer vision techniques typically include image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content/behavior recognition, three-dimensional object reconstruction, 3D techniques, virtual reality, augmented reality, synchronous positioning, and map construction, among others, as well as common biometric recognition techniques such as face recognition, fingerprint recognition, and others.

Key technologies of the voice technology (Speech Technology) are an automatic voice recognition technology and a voice synthesis technology, and a voiceprint recognition technology. The method can enable the computer to listen, watch, say and feel, is the development direction of human-computer interaction in the future, and voice becomes one of the best human-computer interaction modes in the future.

Natural language processing (Nature Language processing, NLP) is an important direction in the fields of computer science and artificial intelligence. It is studying various theories and methods that enable effective communication between a person and a computer in natural language. The natural language processing relates to natural language, namely the language used by people in daily life, and is closely researched with linguistics; and also to computer science and mathematics. An important technique for model training in the artificial intelligence domain, a pre-training model, is developed from a large language model (Large Language Model, LLM) in the NLP domain. Through fine tuning, the large language model can be widely applied to downstream tasks. Natural language processing techniques typically include text processing, semantic understanding, machine translation, robotic questions and answers, knowledge graph techniques, and the like.

Machine Learning (ML) is a multi-domain interdisciplinary, involving multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, etc. It is specially studied how a computer simulates or implements learning behavior of a human to acquire new knowledge or skills, and reorganizes existing knowledge structures to continuously improve own performance. Machine learning is the core of artificial intelligence, a fundamental approach to letting computers have intelligence, which is applied throughout various areas of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, confidence networks, reinforcement learning, transfer learning, induction learning, teaching learning, and the like. The pre-training model is the latest development result of deep learning, and integrates the technology.

In the embodiment of the application, the artificial intelligence technology is applied to the field of asset mapping, and is particularly used for generating target mapping results of corresponding network asset information aiming at target sites so as to realize comprehensiveness and accuracy of asset mapping.

In order to facilitate understanding of the technical solutions provided in the embodiments of the present application, some key terms used in the embodiments of the present application are first explained.

Multimedia information: is a kind of man-machine interactive information communication and propagation medium or a kind of man-machine interactive information communication and propagation medium combining two or more kinds of media. Media includes pictures, text, sound, movies, etc. In the embodiment of the present application, the multimedia information at least includes text information and image information, and of course, the multimedia information may also include audio information, video information, and the like, which may be specifically represented as contents such as pictures, videos, advertisements, articles, information, music, and the like, which is not limited in the present application.

Target subgraph: in the embodiment of the application, the target subgraph is that an extracted image is represented for a specified image in a target image determined as a target class; and the target subgraph is generated according to a geometric figure rule indicated by a preset information coding mode. In particular, common web sites, telephone numbers, email addresses, etc. may be presented in the form of a designated image representation for quick access using the object in a triggered (e.g., scanning, etc.) manner. Illustratively, the designated image representation may be a two-dimensional Code (QR Code), which is: according to an information encoding mode, data information is represented by a graph of black and white intervals regularly distributed on a plane (two-dimensional direction) by a specific geometric figure.

Hypertext markup language (HyperText Markup Language, HTML): is a standard markup language for creating web pages. HTML describes the structure, content, and layout of a document through a series of tags and attributes, thereby enabling cross-platform interactivity and readability of web pages. HTML text is descriptive text made up of HTML commands that can describe elements such as text, images, audio, video, etc.

Transmission control protocol/Internet protocol (Transmission Control Protocol/Internet Protocol, TCP/IP) is a collection of network communication protocols and reference standards that enable various types of computers and devices to communicate and transfer information to and from each other over the Internet.

Secure sockets layer (Secure Socket Layer, SSL): a network transmission security protocol.

Transport layer security (Transport Layer Security, TLS): a standardized transport layer security protocol.

Certificate (Certificate): is a digital certificate for network secure communications.

Hypertext transfer protocol (Hyper Text Transfer Protocol, HTTP): a network application layer protocol.

Hypertext transfer security protocol (Hyper Text Transfer Protocol Secure, HTTPS): protocols for providing website identity authentication and encrypted data transmission using SSL/TLS.

Uniform resource locator (Uniform Resource Locator, URL): is the unique address of a resource on the internet. It can be used to find and retrieve information on a network in a web browser.

Optical character recognition (Optical Character Recognition, OCR): is a technique for converting printed or handwritten text into digital text that can be edited and processed by a computer. OCR technology can convert text in scanned paper, images into editable text.

Internet information service provider (Internet Content Provider, ICP): ICP is a procedure for registering a unit that provides internet information services such as web sites and applications in a specified area by an internet information service provider. The ICP record is for supervising and managing internet information service, and ensuring validity, security and reliability of network content.

The following briefly describes the design concept of the embodiment of the present application.

With the rise of the Internet, the inventor discovers through creative labor that the current asset mapping field has the problem of single mapping dimension. The key point is that, as multimedia information is widely spread on the internet, a large amount of information and data of information security related information such as organizations, enterprises, groups and individuals are widely spread on various websites (sites) of the internet in the form of multimedia information, but a network asset mapping manner combining the multimedia information is currently lacking. In addition, internet sites are currently lacking in ways to learn about the network assets associated with the site (i.e., associated external applications such as public numbers, microblogs, applets, clients, etc.) as a medium and relay for learning about consultation using objects.

In view of this, an embodiment of the present application provides a method for mapping a network asset, in which a multi-dimensional mining manner of network asset information is provided, where a device receives at least one response message sent by a target site based on a network asset request, where the at least one response message includes: the method comprises the steps of extracting target entity information of a target entity to which the target site belongs from text information, extracting external application information of an external application associated with the target site from the image information, and associating the extracted entity object information, the external application information and the obtained site service information into network asset information of the target site to obtain corresponding target mapping results. As can be easily understood, the embodiment of the application focuses not only on the site service information of the target site, but also on the multimedia information borne by the target site, especially the text information and the image information borne by the target site, so that the network asset information of the target site can be more accurately and comprehensively reflected by mapping the text information and the image information; in addition, in the embodiment of the application, on one hand, aiming at text information carried on a target site, a target entity to which the target site belongs is mined, so that the subsequent information safety consideration is facilitated, and corresponding target entity information can be directly obtained based on a target mapping result; on the other hand, aiming at the image information carried on the target site, the external application related to the target site is mined from the image information, so that the subsequent information security consideration is facilitated, the corresponding external application information can be directly obtained based on the target mapping result, and the multi-dimensional mining of the network asset information of the target site is realized.

The following description is made for some simple descriptions of application scenarios applicable to the technical solutions of the embodiments of the present application, and it should be noted that the application scenarios described below are only used for illustrating the embodiments of the present application and are not limiting. In the specific implementation process, the technical scheme provided by the embodiment of the application can be flexibly applied according to actual needs.

The solutions provided by the embodiments of the present application may be applicable to most related asset mapping scenarios, for example: asset mapping scenarios for a target site, etc. As shown in fig. 1, an application scenario is schematically provided in an embodiment of the present application, where the scenario may include a terminal device 110 and a server 120.

The terminal device 110 may be, for example, a mobile phone, a tablet computer (PAD), a notebook computer, a desktop computer, a smart television, a smart car device, a smart wearable device, etc. The terminal device 110 may be installed or connected to an asset mapping platform capable of performing asset mapping, where the asset mapping platform according to the embodiment of the present application may be an applet application, or may be a client such as a web page, software, etc., and the server 120 is a background server corresponding to the applet, the web page, the software, etc., and is not limited to the specific type of the platform.

The server 120 may be a background server corresponding to an asset mapping platform installed or accessed on the terminal device 110, and may implement a background function of network asset mapping to implement the steps of the method for mapping a network asset provided in the embodiments of the present application. For example, the cloud server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, namely, content delivery networks (Content Delivery Network, CDNs), basic cloud computing services such as big data and artificial intelligence platforms, etc., but the present invention is not limited thereto.

The mapping method of the network asset provided in the embodiment of the present application may be performed by the terminal device 110 or the server 120, or may also be performed by a combination of the terminal device 110 and the server 120. Either the terminal device 110 or the server 120 can include one or more processors, memory, and I/O interfaces to interact with the terminals, etc. In addition, the terminal device 110 or the server 120 may also configure a database, or interact with the server 120 configured with the database, where the database may be used to store preset component nodes, component tree disassembly records, and the like. Program instructions of the method for mapping a network asset provided in the embodiment of the present application may also be stored in the memory of the terminal device 110 or the server 120, where the program instructions when executed by the processor can be used to implement steps of the method for mapping a network asset provided in the embodiment of the present application, so as to implement multidimensional mapping on a network asset.

A direct or indirect communication connection may be made between terminal device 110 and server 120 via one or more networks 130. The network 130 may be a wired network, or may be a Wireless network, for example, a mobile cellular network, or may be a Wireless-Fidelity (WIFI) network, or may be other possible networks, which are not limited in this embodiment of the present invention.

In this embodiment, the number of the terminal devices 110 may be one or more, and similarly, the number of the servers 120 may be one or more, that is, the number of the terminal devices 110 or the servers 120 is not limited.

In one possible application scenario, in order to facilitate reducing the communication delay of the search, the servers 120 may be deployed in each region, or for load balancing, different servers 120 may serve the terminal devices 110 in different regions, for example, the terminal device 110 is located at the site a, a communication connection is established with the server 120 serving the site a, the terminal device 110 is located at the site b, a communication connection is established with the server 120 serving the site b, and multiple servers 120 form a data sharing system to implement data sharing through a blockchain.

For each server 120 in the data sharing system having a node identifier corresponding to the server 120, each server 120 in the data sharing system may store the node identifiers of other servers 120 in the data sharing system, so that the generated block may be subsequently broadcast to other servers 120 in the data sharing system according to the node identifiers of the other servers 120. A list of node identifiers may be maintained in each server 120, and the server 120 name and node identifier may be stored in the list of node identifiers. The node identity may be a protocol (Internet Protocol, IP) address of the interconnection between networks, as well as any other information that can be used to identify the node.

Of course, the method provided in the embodiment of the present application is not limited to the application scenario shown in fig. 1, but may be used in other possible application scenarios, and the embodiment of the present application is not limited. The functions that can be implemented by each device in the application scenario shown in fig. 1 will be described together in the following method embodiments, which are not described in detail herein.

The method flow provided in the embodiments of the present application with reference to the drawings may be performed by the server 120 or the terminal device 110 in fig. 1, or may be performed by the terminal device 110 and the server 120 together, and is mainly described herein by taking the server 120 as an example.

Referring to fig. 2, a flow chart of an application development method provided in an embodiment of the present application is shown.

Step 201: receiving at least one response message sent by a target site based on a network asset request; wherein, the at least one response message comprises: the system comprises site service information of a target site and multimedia information carried by the target site, wherein the multimedia information at least comprises text information and image information.

Wherein the site service information of the target site includes, but is not limited to: IP address, service port, service protocol, SSL/TLS certificate, key information of SSL/TLS certificate (e.g., organization name, geographic location, domain name information, mailbox, IP list, etc.).

In a possible implementation manner, referring to fig. 3, a schematic process of acquiring site service information in an embodiment of the present application is shown; the terminal equipment responds to the establishment of network connection with the target site, receives response information sent by the target site aiming at the site service information request, and analyzes the site service information such as the service IP address, the service port, the service protocol and the like of the target site according to the TCP/IP network transmission protocol information contained in the response information aiming at the response information when the response information comprises the TCP/IP network transmission protocol information.

Optionally, as shown in fig. 3, when the service protocol is HTTPS service, the asset data of the SSL/TLS digital certificate of the service end where the target site is located is obtained, and based on this, key information of the SSL/TLS digital certificate, for example, an organization name, a geographic location, domain name information, a mailbox, an IP list, and the like of the SSL/TLS digital certificate are resolved and extracted, which are together used as corresponding site service information.

Further, the terminal device receives response information sent by the target site for the site content information request in response to establishing the network connection with the target site, analyzes the load multimedia information about the target site in the response information, and obtains each dynamic or static multimedia information carried by the target site, where the dynamic or static multimedia information at least includes text information and image information, and of course, the terminal device may further include: file information, audio information, video information, etc., which are not particularly limited in the embodiments of the present application.

It should be noted that, the request information, such as the site service information request, the site content request, etc., may be generated after determining the access address of the target site, where the access address includes, but is not limited to: the website domain name, IP address, or URL of the target site. Alternatively, the request information may be generated by constructing a website for the target site by using a legal and compliant website crawler technology, or may be generated by simulating a browser to send a request to access the target site.

Step 202: extracting target entity information of a target entity to which the target site belongs from the text information; and extracting external application information of the external application associated with the target site from the image information.

In the embodiment of the application, the multimedia information at least comprises text information and image information, and the multi-dimensional mapping of the network asset information of the target site is realized by mining the multimedia information carried by the target site, so that the mining process of the network asset information is realized by dividing the information into two parts (such as a first part and a second part) for respectively describing the text information and the image information for the convenience of understanding.

And the first part is used for extracting target entity information of a target entity to which the target site belongs from the text information.

The target entity to which the target site belongs may be: owners, registrants, operators, etc. of targeted sites (websites/domains). The target entity information is description information of the target entity, and the target entity information may include, but is not limited to: organization name, website ICP record number, network security record number, government website identification code, mailbox information, telephone information, communication address.

Fig. 4 is a schematic diagram of a process for obtaining target entity information in an embodiment of the present application; wherein, based on natural language processing technology and pattern matching technology, extracting target entity information related to the target entity to which the text information belongs, for example: the method comprises the steps of organizing a name, a website ICP record number, a network security record number, a government website identification code, mailbox information, telephone information, a communication address and the like by a unit, converting the obtained target entity information into a structured text and storing the structured text.

Specifically, word segmentation processing is performed on text information based on a natural language processing technology to obtain a corresponding word segmentation result, and then target word segmentation related to a target entity is selected from the word segmentation result based on a pattern matching technology to obtain corresponding target entity information.

It should be noted that, the text information participating in the word segmentation process includes, but is not limited to: text titles in the target site, HTML text content carried by the target site, and other text content carried by the target site.

And a second part extracting external application information of an external application associated with the target site from the image information.

Wherein the external application information includes, but is not limited to: application description information (such as mailbox, address, etc.), application platform information (such as public number, applet, client, webpage, etc.), application link information (such as address code information, etc.) of web application of the external application.

Specifically, at least one target image belonging to a target category is selected in the image information based on a preset target classification index, wherein the target classification index characterizes: for one classification mode of the target category, then for at least one target image, the following operations are respectively executed: extracting at least one target subgraph in a designated graphic representation in one target image, wherein each target subgraph: the method comprises the steps of generating according to a geometric figure rule indicated by a preset information coding mode, and respectively carrying out image coding analysis processing on each extracted target subgraph based on external application associated with each target subgraph to obtain external application information of a target site.

The target subgraph is an image extracted from a target image determined as a target type, and is expressed by a specified image; and the target subgraph is generated according to a geometric figure rule indicated by a preset information coding mode. In detail, the target subgraph may include information such as web address, telephone number, email address, etc., and the target subgraph is presented in a specified image representation, and may be triggered for quick access by the user. The specified image representation may be in the form of a two-dimensional code, the target category is a two-dimensional code, the target subgraph is a screenshot of the two-dimensional code, and the two-dimensional code represents data information by using a graph with black and white intervals regularly distributed on a plane (two-dimensional direction) according to a specific geometric figure in an information coding mode.

In an optional implementation manner, the image information further includes text information, and further, an optical character recognition manner is adopted to convert the text information in the image information from image representation to text representation, so as to obtain text representation in the text information, then, in the text representation, entity description information of an associated entity of the target site is extracted, the entity description information is stored, and the entity description information is subsequently associated as network asset information of the target site.

Further, if the text information in the image information is watermark text, further, content recognition (image recognition) technology is adopted to extract the watermark text therein to obtain corresponding text representation, and then entity description information (such as telephone, ID nickname, name, etc.) related to the associated entity of the target site is extracted.

In an alternative implementation, image description information of each image may also be extracted for the image information, where the image description information includes, but is not limited to: image format type (e.g., PNG, JPG, GIF), image type, image resolution, image acquisition geographic location, text content contained in the image.

In summary, as a more complete embodiment, referring to fig. 5, a schematic process for extracting image information in the embodiment of the present application is shown. Firstly, extracting image description information (such as image format type, image resolution, image acquisition geographic position and other text content) of each image aiming at the image information; secondly, extracting watermark characters from the image information by utilizing a content identification technology to serve as entity description information (such as a telephone, an ID nickname, a name and the like) related to the associated entity of the target site; thirdly, at least one target subgraph in a specified graphic representation (such as a two-dimensional code form) is extracted aiming at the target image, and external application information of the target site is obtained by carrying out image coding analysis processing on each extracted target subgraph.

Further, in an alternative implementation manner, the multimedia information may further include any one of file information, audio information and video information, and based on the two parts, the multi-dimensional mapping of the network asset information of the target site may be extended to three parts (such as a third part, a fourth part and a fifth part) to respectively describe the file information, the audio information and the video information, so as to implement the mining process of the network asset information.

And a third section for extracting, from the file information, respective document file information of each document file carried by the target site, if the multimedia information further includes file information including at least one document file.

Among them, file formats of the document file include, but are not limited to: office, PDF format, markdown. Document file information of a document file including document description information and document content information, wherein the document description information includes, but is not limited to: document author, document release time, organization unit information, contact information, document content information including, but not limited to: document title, document keywords, document content summary.

Referring to fig. 6, a schematic diagram of a process for obtaining information of a document file according to an embodiment of the present application is shown. Wherein, for at least one document file in the file information, the following operations are performed, respectively: acquiring document description information of a document file, extracting document content information of the document file, acquiring corresponding document file information, storing the extracted document file information, and associating the document file information corresponding to at least one document file as network asset information of a target site.

The fourth part, the multimedia information also includes audio information, the audio information includes at least one audio file, and the audio file information of each audio file carried by the target site is extracted from the audio information.

Among them, file formats of audio files include, but are not limited to: WMV, MP3, AVI. Audio file information for an audio file, including audio description information and audio content information, wherein the audio description information includes, but is not limited to: audio format, audio duration, audio type, audio content information including, but not limited to: audio names, audio keywords, phonetic text in audio, short text abstracts (which can be used to reduce the occupation of storage space and improve the analysis efficiency of the network asset information for the target site).

Fig. 7 is a schematic diagram of a process for obtaining audio file information according to an embodiment of the present application. Wherein, for at least one audio file in the audio files, the following operations are performed respectively: the method comprises the steps of obtaining audio description information of one audio file, extracting audio content information of one audio file, obtaining corresponding audio file information, storing the extracted audio file information, and associating the audio file information corresponding to at least one audio file as network asset information of a target site.

And fifth, extracting the video file information of each video file carried by the target site from the video information when the multimedia information also comprises the video information.

In one possible implementation, the video information includes at least one video file, and each video file includes: and (3) a plurality of continuous image frames, respectively executing the following operations for at least one video file: the method comprises the steps of obtaining video description information of a video file, extracting video content information of a head image frame and a tail image frame aiming at the video file, obtaining corresponding video file information, storing the obtained video file information, and associating the video file information corresponding to at least one video file into network asset information of a target site.

Wherein, the video description information includes but is not limited to: video formats (e.g., MP4, AVI, WMV, FLV, RMVB, etc.), video duration, video frame rate, video resolution, including but not limited to: video name, video type, video frame summary, video text summary, other text information within the video, head image frames, tail image frames.

It should be noted that, the number of frames of the head image frame and the tail image frame may be determined according to actual situations, and the head image frame and the tail image frame may include: the related information of the video producer, etc., are not particularly limited herein.

Optionally, one video file further comprises: and performing voice recognition processing on each audio frame in a video file by adopting a voice recognition mode to obtain corresponding voice text representation, extracting entity description information of an associated entity of the target site from the voice text representation, storing the entity description information, and subsequently associating the entity description information into network asset information of the target site.

Wherein, the entity description information includes but is not limited to: entity name, entity ID, unit introduction information, contact information, audio summary, etc.

Optionally, one video file further comprises: and if the caption information corresponding to the plurality of image frames is caption information, caption extraction processing is carried out on each piece of caption information in one video file by adopting a caption extraction technology, corresponding caption text representation is obtained, entity description information of the associated entity of the target site is extracted from the caption text representation, the entity description information is stored, and the entity description information is subsequently associated as network asset information of the target site.

Wherein, the entity description information includes but is not limited to: video subtitle text, video subtitle summary.

It should be noted that, when the image frame in one video file is the target type, that is, the target image described in the second part, the related content of the second part is executed, and the description is not repeated here.

In summary, as a more complete embodiment, referring to fig. 8, a schematic diagram of a process for extracting video information in an embodiment of the present application is shown. Firstly, extracting video description information (such as video format, video duration, video frame rate, video resolution and the like) of each video file aiming at the video information; secondly, extracting video content information (such as video names, video types, video frame abstracts, video text abstracts, other text information in video, head image frames and tail image frames) of head image frames and tail image frames for each video file by utilizing a video processing technology and an image recognition technology; thirdly, extracting entity description information (such as entity name, entity ID, unit introduction information, contact information, audio abstract and the like) of an associated entity of the target site by adopting a voice recognition mode aiming at each audio frame in each video file; fourthly, extracting entity description information (such as video caption text, video caption abstract and the like) of the associated entity of the target site aiming at each piece of caption information in each video file by adopting a caption extraction technology.

Step 203: and associating the entity object information, the external application information and the site service information into network asset information of the target site so as to obtain a corresponding target mapping result.

Alternatively, the multimedia information of the target site includes text information, image information, file information, audio information, and video information, and the corresponding target mapping result can be seen in fig. 9.

As shown in fig. 9, the target mapping results are stored in structured text, where the first line includes: title information of the site service information, and title information from the multimedia information, each of which is extraction information under the corresponding title information, are described in detail below.

The site service information includes: service IP, hostname, service port, service protocol, service application, HTTPS credentials, other information.

The information extracted for the text information (namely, entity object information of the target entity to which the target site belongs) comprises: website URL, website title, navigation menu, text content, website fingerprint, ICP record number, public security network security record number, government website identification code, unit name, communication address, mailbox, telephone, other information.

The information extracted for the file information includes: file URL, file name, file format, file size, file author, file time, file fingerprint, file title, file key, file content digest, organization unit, containing picture, containing audio, containing video, other information.

The information extracted for the image information includes: picture URL, picture name, picture format, picture size, resolution, picture time, picture fingerprint, geographical location, picture type, picture text, external application information (e.g., public number two-dimensional code, applet two-dimensional code, client two-dimensional code, other two-dimensional code), picture watermark, other information.

The information extracted for the audio information includes: audio URL, audio name, audio format, audio size, sample rate, audio duration, modification time, phonetic text, keywords, summary, audio fingerprint, other information.

The information extracted for the video information includes: video URL, video name, video format, video size, frame rate, video duration, modification time, video fingerprint, video classification, video subtitle text, video voice text, header image frame, trailer image frame, external application information (e.g., public number two-dimensional code, applet two-dimensional code, client two-dimensional code, other two-dimensional code), video summary, other information.

In summary, the embodiments of the present application provide a method for mapping a network asset, which performs more dimensional asset data mining on resource content and transport layer data included in a website webpage of a target site, including resources such as TCP/IP protocol, TLS/SSL digital certificate, text, file, picture, audio and video, so that mapping resource types are more comprehensive, mapping content is deeper, asset dimension data is richer, latest mature technologies such as natural language processing technology, picture-to-text, audio processing, video processing and the like can be fully utilized, acquired asset information clues are more accurate and richer, and finally a multi-dimensional information asset database is built for the network asset, so that more comprehensive asset association and key clue information of asset discovery can be provided, the effect of network asset discovery and asset association is improved, and more roles are played in network space mapping, and the method specifically includes the following steps:

1. In the aspect of mapping for, for example, website digital assets (i.e., network resource information of a target site), the embodiment of the application comprehensively collects resource content and transport layer data contained in website webpages of the target site, including resources such as TCP/IP protocol, TLS/SSL digital certificates, text, files, pictures, audio and video, and the like, has more mapping dimensions and more comprehensive mapping resource type coverage; the mapping content is deeper, and the latest mature technologies such as natural language processing technology, picture-to-text, audio processing, video processing and the like are adopted, so that the information clues of the mapped asset are more accurate and rich.

2. When mapping website digital assets (namely network asset information) of a target site, the embodiment of the application utilizes emerging technologies such as picture classification and identification to deeply extract picture asset key information in the website, and carries out identification analysis on two-dimension code type pictures, so that mobile assets such as emerging WeChat public numbers, microblog account numbers, applet account numbers, clients and the like can be associated, important data assets such as pictures are mapped and acquired, and information clues contained in website picture digital asset data are expanded.

3. When mapping website digital assets (namely network asset information) of a target site, key information of audio and video assets in the website is deeply extracted by using emerging technologies such as voice recognition, video processing and the like, key information extraction and abstract generation are respectively carried out on audio and video, mapping and acquisition are carried out on important data assets such as audio and video, and information clues contained in website audio digital asset data are expanded.

4. When mapping the website digital assets (namely network asset information) of the target site, the embodiment of the application deeply extracts the key information of the text file, forms the meta information of the refined text file, covers the data assets such as the file in mapping acquisition, and expands the clue information contained in the website text data.

Referring to fig. 10, based on the same inventive concept, an embodiment of the present application further provides a mapping apparatus 1000 of a network asset, including:

a receiving unit 1001, configured to receive at least one response message sent by a target site based on a network asset request; wherein, the at least one response message comprises: the method comprises the steps of site service information of a target site and multimedia information carried by the target site, wherein the multimedia information at least comprises text information and image information;

an extracting unit 1002, configured to extract, from the text information, target entity information of a target entity to which the target site belongs; extracting external application information of external applications associated with the target site from the image information;

and an obtaining unit 1003, configured to associate the entity object information, the external application information, and the site service information to be network asset information of the target site, so as to obtain a corresponding target mapping result.

Optionally, the extracting unit 1002 is configured to extract, from the image information, external application information of an external application associated with the target site, specifically configured to:

selecting at least one target image belonging to a target category from the image information based on a preset target classification index; wherein, target classification index characterizes: a classification scheme for the target class;

for at least one target image, the following operations are performed: extracting at least one target sub-graph represented by a designated graph from one target image; wherein each target subgraph: is generated according to the geometric figure rule indicated by the preset information coding mode;

and respectively carrying out image coding analysis processing on each extracted target subgraph based on the external application associated with each target subgraph to obtain external application information of the target site.

Optionally, if the image information includes text information, the extracting unit 1002 is further configured to:

and extracting entity description information of the associated entity of the target site from the text representation, and associating the entity description information as network asset information of the target site.

Optionally, the multimedia information further includes file information, the file information including at least one document file;

the extracting unit 1002 is further configured to:

for at least one document file, the following operations are performed, respectively: acquiring document description information of a document file, extracting document content information of the document file, and acquiring corresponding document file information;

and associating the document file information corresponding to each of the at least one document file as network asset information of the target site.

the extracting unit 1002 is further configured to:

for at least one audio file, the following operations are performed: acquiring audio description information of an audio file, extracting audio content information of the audio file, and acquiring corresponding audio file information;

Optionally, the multimedia information further comprises video information, the video information comprising at least one video file, each video file comprising: a plurality of consecutive image frames;

The extracting unit 1002 is further configured to:

for at least one video file, the following operations are performed: acquiring video description information of a video file, extracting video content information of a head image frame and a tail image frame aiming at the video file, and acquiring corresponding video file information;

and associating the video file information corresponding to each of the at least one video file as network asset information of the target site.

Optionally, one video file further comprises: audio frames corresponding to the plurality of image frames;

the extracting unit 1002 is further configured to:

performing voice recognition processing on each audio frame in a video file by adopting a voice recognition mode to obtain corresponding voice text representation;

a video file further comprising: subtitle information corresponding to the plurality of image frames;

the extracting unit 1002 is further configured to:

performing subtitle extraction processing on each subtitle information in a video file by adopting a subtitle extraction technology to obtain a corresponding subtitle text representation;

The apparatus may be used to perform the methods shown in the embodiments of the present application, so the descriptions of the foregoing embodiments may be referred to for the functions that can be implemented by each functional module of the apparatus, and are not repeated.

Referring to fig. 11, based on the same technical concept, the embodiment of the present application further provides a computer device 1100, where the computer device 1100 may be a terminal device or a server shown in fig. 1, and the computer device 1100 may include a memory 1101 and a processor 1102.

Memory 1101 for storing computer programs executed by processor 1102. The memory 1101 may mainly include a storage program area that may store an operating system, application programs required for at least one function, and the like, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. The processor 1102 may be a central processing unit (central processing unit, CPU), or a digital processing unit or the like. The specific connection medium between the memory 1101 and the processor 1102 is not limited in the embodiments of the present application. In the embodiment of the present application, the memory 1101 and the processor 1102 are connected by a bus 1103 in fig. 11, the bus 1103 is shown by a thick line in fig. 11, and the connection manner between other components is only schematically illustrated, and is not limited thereto. The bus 1103 can be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 11, but not only one bus or one type of bus.

The memory 1101 may be a volatile memory (RAM), such as a random-access memory (RAM); the memory 1101 may also be a nonvolatile memory (non-volatile memory), such as a read-only memory, a flash memory (flash memory), a Hard Disk Drive (HDD) or a Solid State Drive (SSD), or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto. The memory 1101 may be a combination of the above memories.

A processor 1102, configured to execute the methods executed by the apparatus in the embodiments of the present application when invoking the computer program stored in the memory 1101.

In some possible implementations, aspects of the methods provided herein may also be implemented in the form of a program product comprising program code for causing a computer device to perform the steps of the methods described herein above according to the various exemplary embodiments of the application, when the program product is run on a computer device, e.g. the computer device may perform the methods performed by the devices in the various embodiments of the application.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.

Claims

1. A method of mapping a network asset, the method comprising:

2. The method of claim 1, wherein extracting external application information of an external application associated with the target site from the image information comprises:

3. The method of claim 1, wherein the image information includes text information, and the method further comprises:

4. A method according to any one of claims 1 to 3, wherein the multimedia information further comprises file information, the file information comprising at least one document file;

The method further comprises:

5. A method according to any one of claims 1 to 3, wherein the multimedia information further comprises audio information, the audio information comprising at least one audio file;

the method further comprises:

6. A method according to any one of claims 1 to 3, wherein the multimedia information further comprises video information, the video information comprising at least one video file, each video file comprising: a plurality of consecutive image frames;

The method further comprises:

7. The method of claim 6, wherein the one video file further comprises: audio frames corresponding to the plurality of image frames;

the method further comprises:

8. A mapping apparatus for a network asset, the apparatus comprising:

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that,

The processor, when executing the computer program, implements the steps of the method of any one of claims 1 to 7.

10. A computer storage medium having stored thereon computer program instructions, characterized in that,

the computer program instructions, when executed by a processor, implement the steps of the method of any of claims 1 to 7.