CN115952305A - Fusion method and device of information, computing equipment and storage medium - Google Patents
Fusion method and device of information, computing equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention relates to the technical field of information security, in particular to a fusion method and device of information, computing equipment and a storage medium. The method comprises the following steps: acquiring a plurality of pieces of information; each information message comprises a name field, a basic attribute field and an association relation field; based on a pre-constructed standard dictionary table, carrying out standard field conversion on a name field, a basic attribute field and an association relation field in each information message; carrying out information fusion on the information with the same name field after the standard field is converted so as to generate target information through fusion; and filtering the incidence relation in the target information based on a pre-constructed relation mode table to obtain standard information. The scheme can automatically update and fuse more accurate standard information.
Description
Technical Field
The embodiment of the invention relates to the technical field of information security, in particular to a fusion method and device of information, computing equipment and a storage medium.
Background
At present, the same information exists in a plurality of information sources, the timeliness of the multi-source information is strong, the updating iteration is fast, and the structure and the expression of the same information in different information sources are also various. Therefore, it is difficult to obtain accurate and effective information by processing multi-source information only by the conventional fusion method of replacement and deduplication.
Therefore, a new method for fusing informative information is needed.
Disclosure of Invention
In order to solve the problem that the accuracy of the information is difficult to guarantee in the traditional fusion method, the embodiment of the invention provides a fusion method and device of the information, computing equipment and a storage medium.
In a first aspect, an embodiment of the present invention provides a method for fusing intelligence information, including:
acquiring a plurality of pieces of information; each information message comprises a name field, a basic attribute field and an association relation field;
based on a pre-constructed standard dictionary table, carrying out standard field conversion on a name field, a basic attribute field and an association relation field in each information message;
carrying out information fusion on the information with the same name field after the standard field is converted so as to generate target information through fusion;
and filtering the incidence relation in the target information based on a pre-constructed relation mode table to obtain standard information.
Preferably, based on a pre-constructed standard dictionary table, standard field conversion is performed on the name field, the basic attribute field and the association relation field in each intelligence information, and the standard field conversion includes:
generating an original information table based on a name field, a basic attribute field and an incidence relation field in the information;
carrying out similarity analysis on each name field, each basic attribute field and each incidence relation field in the original information table and each standard field in a pre-constructed standard dictionary table to generate a mapping relation table;
and based on the mapping relation table, carrying out standard field conversion on the name field, the basic attribute field and the association relation field of each intelligence information in the original information table.
Preferably, the similarity analysis is performed in any one of the following ways: cosine similarity, euclidean distance.
Preferably, the information fusion of the information with the same name field after the standard field conversion is performed to generate the target information by fusion, and the method includes:
sorting the information with the same name field after the standard field is converted;
according to the descending order, aiming at each information after the first information, executing:
comparing the basic attribute field and the incidence relation field of the current information with the basic attribute field and the incidence relation field of the first information respectively;
if the current information has a basic attribute field and an association relation field which are different from the first information, the different basic attribute field and the association relation field are supplemented into the first information;
the supplemented first information is used as target information.
Preferably, the sorting of the intelligence information with the same name field after the standard field conversion comprises:
acquiring the information source weight of the information with the same name field after the standard field is converted;
and sorting the information with the same name field after the standard field is converted according to the descending order of the information source weight.
Preferably, after sorting the intelligence information with the same name field after converting the standard field according to the descending order of the intelligence source weight, the method further comprises:
when the information source weights of the information are the same, sorting is performed according to the generation time of the information.
Preferably, based on a pre-constructed relationship schema table, the method filters the relationship in the target information to obtain the standard information, and includes:
acquiring a pre-constructed relation mode table; wherein, the relation mode table contains the type of the incidence relation to be reserved for each type of target information;
and filtering the association relation in the target information to obtain standard information based on the type of the target information and the type of the association relation to be reserved in each type of target information in the relation mode table.
In a second aspect, an embodiment of the present invention further provides an apparatus for fusing information, including:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a plurality of pieces of information; each information message comprises a name field, a basic attribute field and an association relation field;
the normalization unit is used for carrying out standard field conversion on the name field, the basic attribute field and the incidence relation field in each information based on a pre-constructed standard dictionary table;
the fusion unit is used for carrying out information fusion on the information with the same name field after the standard field is converted so as to generate target information through fusion;
and the filtering unit is used for filtering the incidence relation in the target information based on a pre-constructed relation mode table to obtain standard information.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the processor executes the computer program to implement the method according to any embodiment of this specification.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed in a computer, the computer program causes the computer to execute the method described in any embodiment of the present specification.
The embodiment of the invention provides a fusion method, a fusion device, a computing device and a storage medium of information, which comprises the steps of firstly, obtaining a plurality of information containing name fields, basic attribute fields and incidence relation fields; then, based on a pre-constructed standard dictionary table, carrying out standard field conversion on the name field, the basic attribute field and the association relation field in each information message so as to convert the name field, the basic attribute field and the association relation field of each information message into a unified standard field; then, carrying out information fusion on the information with the same name field after the standard field is converted so as to generate corresponding target information through fusion; and finally, filtering the incidence relation in each target information message generated by fusion based on the relation mode table to obtain standard information messages corresponding to each target information message, thereby automatically updating and fusing more accurate standard information messages.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of an intelligence information fusion method according to an embodiment of the present invention;
FIG. 2 is a diagram of a hardware architecture of a computing device according to an embodiment of the present invention;
fig. 3 is a structural diagram of an information fusion apparatus according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, it is obvious that the described embodiments are some, but not all embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
As described above, in some conventional methods for fusing information, information needs to be merged into multiple sources of information, but each source of information has an incomplete definition for the same information, so that different representations of the same information may be generated. Then, as the historical data is accumulated, the similar expressions of the same information are increased, so that the information fused by the traditional information fusion method has a large amount of inaccurate expressions, and the related information which is not used any more is not automatically deleted, so that the traditional fusion method can generate a large amount of redundant and wrong information.
In order to solve the above technical problems, the inventor may consider that a pre-constructed standard dictionary table is utilized to automatically perform standard field conversion on the information, so as to perform unified standard conversion on the name field, the basic attribute field and the association relation field of each information, then the converted multi-source information is fused into a target information, and finally the association information which is not used any more in the target information is filtered based on the relation mode table, so as to automatically update and fuse more accurate standard information.
Specific implementations of the above concepts are described below.
Referring to fig. 1, an embodiment of the present invention provides a method for fusing information, where the method includes:
step 100: acquiring a plurality of pieces of information; each information message comprises a name field, a basic attribute field and an association relation field;
step 102: based on a pre-constructed standard dictionary table, carrying out standard field conversion on a name field, a basic attribute field and an association relation field in each information message;
step 104: carrying out information fusion on the information with the same name field after the standard field is converted so as to generate target information through fusion;
step 106: and filtering the incidence relation in the target information based on a pre-constructed relation mode table to obtain standard information.
In the embodiment of the invention, firstly, a plurality of pieces of information containing name fields, basic attribute fields and incidence relation fields are obtained; then, based on a pre-constructed standard dictionary table, carrying out standard field conversion on the name field, the basic attribute field and the association relation field in each information message so as to convert the name field, the basic attribute field and the association relation field of each information message into a unified standard field; then, carrying out information fusion on the information with the same name field after the standard field is converted so as to generate corresponding target information through fusion; and finally, filtering the incidence relation in each target information message generated by fusion based on the relation mode table to obtain standard information messages corresponding to each target information message, thereby automatically updating and fusing more accurate standard information messages.
The manner in which the various steps shown in fig. 1 are performed is described below.
With respect to step 100:
in the embodiment of the invention, multi-source information possibly exists in the obtained plurality of information, namely the same information from different information sources. For example, by taking the sea lotus of the threat organization as an example, some information sources define the name field of the sea lotus of the threat organization as APT-TOCS, some information sources as the sea lotus/APT-C-00, and some information sources as APT32, so that in the information about the sea lotus of the threat organization obtained by different information sources, not only the names are different, but also the basic attributes and the association relation are possibly different.
With respect to step 102:
as described above, the multiple information is the same information from different information sources, and describes the same organization, but there are cases where the name fields are different, the basic attribute fields are different, and the association relation fields are different. Then, in order to perform accurate information fusion on multi-source intelligence information, it is necessary to perform standard normalization on the name field, the basic attribute field, and the association relation field of each intelligence information.
In some embodiments, step 102 may comprise:
generating an original information table based on a name field, a basic attribute field and an incidence relation field in the information;
carrying out similarity analysis on each name field, each basic attribute field and each incidence relation field in the original information table and each standard field in a pre-constructed standard dictionary table to generate a mapping relation table;
and based on the mapping relation table, carrying out standard field conversion on the name field, the basic attribute field and the association relation field of each intelligence information in the original information table.
In this embodiment, the name field, the basic attribute field, and the association field of each piece of information are first entered into the original information table, so as to perform standard field transformation on the name field, the basic attribute field, and the association field of each piece of information in the original information table, thereby preventing the original information from being directly transformed to cause information loss. The standard dictionary table contains standard fields corresponding to the name field, the basic attribute field and the association relation field of each intelligence information. For example, in the standard dictionary table, the standard field corresponding to the name of threat organization a may be set to "a", and the information 1 obtained from the information source 1 defines its name field as "A1", and the information 2 obtained from the information source 2 defines its name field as "A2", then the similarity analysis is performed between the name field "A1" of the information 1 obtained from the original information table and each standard field in the standard dictionary table, and it can be determined that the similarity between the name field "A1" of the information 1 and the standard field "a" is the largest. At this time, a mapping relationship between the standard fields "a" and "A1" is created in the mapping relationship table, and similarly, a mapping relationship between the standard fields "a" and "A2" is also created in the mapping relationship table. In this way, the name field of the information 1 in the original information table can be converted from "A1" to "a" and the name field of the information 2 can be converted from "A2" to "a" based on the mapping relationship table. It will be appreciated that the standard field transformation for the base attribute fields and the association fields is the same as for the name fields described above.
It should be noted that the standard dictionary table is artificially constructed in advance based on known informative information. And the mapping relation table in the initial state is empty, the complementary mapping relation is automatically and gradually completed along with the practical application, and the completion mode comprises the modes of semantic parsing, semantic analysis, statistical analysis, manual correction and the like.
In some embodiments, the similarity analysis is performed in any one of the following ways: cosine similarity, euclidean distance.
In some embodiments, after the step "generating a mapping relation table", before "performing standard field conversion on the name field, the base attribute field, and the association relation field of each intelligence information in the original information table based on the mapping relation table", the method further includes: and auditing and correcting the mapping relation in the mapping relation table.
In this embodiment, in order to improve the accuracy of the mapping relationship, the mapping relationship in the mapping relationship table may be artificially checked and corrected, so as to improve the accuracy of standard field conversion of the information, thereby solving the problem of redundancy and error information existing in the fused information.
With respect to step 104:
in some embodiments, step 104 may include steps S1-S4 as follows:
s1, sorting the information with the same name field after the standard field is converted;
step S2, according to the descending order, aiming at each information after the first information, executing: comparing the basic attribute field and the incidence relation field of the current information with the basic attribute field and the incidence relation field of the first information respectively;
step S3, if the current information has the basic attribute field and the incidence relation field which are different from the first information, the different basic attribute field and the incidence relation field are supplemented to the first information
And step S4, using the supplemented first information as target information.
In this embodiment, in order to facilitate subsequent information fusion, the intelligence information with the same name field after the standard field conversion needs to be sorted first, and the sorting mode may be random sorting. For example, there are three pieces of sorted information about threat organization a, information 1, information 2 and information 3 are sequentially sorted, if information 2 contains basic attribute B1 and association relationship C1, and information 1 does not contain basic attribute B1 and association relationship C1, then basic attribute B1 and association relationship C1 are added to information 1, and information 3 does not contain basic attribute B1 and association relationship C1, and also contains association relationship C2 that information 1 does not contain, then after adding basic attribute B1 and association relationship C1 of information 2, association relationship C2 in information 3 is added to information 1, and finally obtained information 1 is used as target information after threat organization a is fused.
In some embodiments, step S1 may comprise:
acquiring the information source weight of the information with the same name field after the standard field is converted;
and sorting the information with the same name field after the standard field is converted according to the descending order of the information source weight.
In this embodiment, since the richness and effectiveness of the intelligence information generated by different intelligence sources are different, the intelligence information in the group needs to be sorted according to the intelligence source weight corresponding to each intelligence information. For example, if the intelligence source weight of intelligence information 1 is 100 and the intelligence source weights of intelligence information 2 and intelligence information 3 are 50 and 80, respectively, then the quality level of the intelligence information is represented by: information 1, information 3 and information 2, and the sequencing result is also as follows in sequence: information 1, information 3, and information 2. If the content of the basic attribute B0 in the information 1 is not consistent with the content of the basic attribute B0 in the information 2, the basic attribute and the association relation in the high-quality information are preferentially used according to the information 1, so that the accuracy of the target information obtained after fusion can be improved.
In some embodiments, after the step of sorting the plurality of intelligence information in the group in order of big to small intelligence source weight, the method further comprises: when the information source weights of the information are the same, sorting is performed according to the generation time of the information.
In this embodiment, there may be a case where the intelligence source weights of a plurality of pieces of intelligence information are the same, and then when the intelligence source weights of the intelligence information are the same, sorting may be performed according to the generation time of the intelligence information. For example, if the information source weight of the information 1 is 100 and the information source weights of the information 2 and the information 3 are both 80, the information 2 and the information 3 can be sorted according to their generation times, and the information with the later generation time can be arranged in the second place, so that the basic attribute and the association relationship in the information with the closer generation time can be preferentially used, and the timeliness of the target information obtained after fusion can be improved.
For step 106:
in some embodiments, step 106 may comprise:
acquiring a pre-constructed relation mode table; wherein, the relation mode table contains the type of the incidence relation to be reserved for each type of target information;
and filtering the association relation in the target information to obtain standard information based on the type of the target information and the type of the association relation to be reserved for each type of target information in the relationship mode table.
In the embodiment, since the timeliness of the information is strong, the update iteration is fast, and many association relations may be invalid at present, the type of the association relation that needs to be retained in the relationship pattern table can be dynamically modified, so as to filter out the association relation that does not need to be retained in each target information obtained by fusion in step 104, thereby reducing the redundancy of the target information, and dynamically adjusting the association relation of the standard information according to the relationship pattern table.
As shown in fig. 2 and fig. 3, an embodiment of the present invention provides an information fusion apparatus. The device embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. From a hardware aspect, as shown in fig. 2, a hardware architecture diagram of an electronic device in which an intelligence information fusion apparatus provided in an embodiment of the present invention is located is shown, where in addition to the processor, the memory, the network interface, and the nonvolatile memory shown in fig. 2, the electronic device in which the apparatus is located in the embodiment may generally include other hardware, such as a forwarding chip responsible for processing a message, and the like. Taking a software implementation as an example, as shown in fig. 3, as a logical device, a CPU of the electronic device reads a corresponding computer program in the non-volatile memory into the memory for running.
As shown in fig. 3, the fusion apparatus of information provided in this embodiment includes:
an obtaining unit 301, configured to obtain a plurality of pieces of information; each information message comprises a name field, a basic attribute field and an association relation field;
the normalization unit 302 is configured to perform standard field conversion on a name field, a basic attribute field and an association relation field in each piece of information based on a pre-constructed standard dictionary table;
the fusion unit 303 is configured to perform information fusion on the information with the same name field after the standard field is converted, so as to generate target information through fusion;
and the filtering unit 304 is configured to filter the association relationship in the target information based on a pre-constructed relationship pattern table to obtain standard information.
In an embodiment of the invention, the normalization unit 302 is configured to perform:
generating an original information table based on a name field, a basic attribute field and an incidence relation field in the information;
carrying out similarity analysis on each name field, each basic attribute field and each association relation field in the original information table and each standard field in a pre-constructed standard dictionary table to generate a mapping relation table;
and based on the mapping relation table, carrying out standard field conversion on the name field, the basic attribute field and the association relation field of each intelligence information in the original information table.
In an embodiment of the present invention, in the normalization unit 302, the similarity analysis is performed in any one of the following manners: cosine similarity, euclidean distance.
In an embodiment of the present invention, the merging unit 303 is configured to perform:
sorting the information with the same name field after the standard field is converted;
according to the descending order, aiming at each information after the first information, executing:
comparing the basic attribute field and the incidence relation field of the current information with the basic attribute field and the incidence relation field of the first information respectively;
if the current information has a basic attribute field and an association relation field which are different from the first information, the different basic attribute field and the association relation field are supplemented into the first information;
the supplemented first information is used as target information.
In an embodiment of the present invention, the merging unit 303, when performing sorting of the informative information with the same name field after converting the standard field, is configured to perform:
acquiring the information source weight of the information with the same name field after the standard field is converted;
and sorting the information with the same name field after the standard field is converted according to the descending order of the information source weight.
In an embodiment of the present invention, the merging unit 303, after performing sorting of the intelligence information with the same name field after converting the standard field according to the descending order of the intelligence source weight, is further configured to: when the information source weights of the information are the same, sorting is performed according to the generation time of the information.
In an embodiment of the present invention, the filtering unit 304 is configured to perform:
acquiring a pre-constructed relation mode table; wherein, the relation mode table contains the type of the incidence relation to be reserved for each type of target information;
and filtering the association relation in the target information to obtain standard information based on the type of the target information and the type of the association relation to be reserved for each type of target information in the relationship mode table.
It is to be understood that the illustrated structure of the embodiment of the present invention does not specifically limit an information fusion apparatus. In other embodiments of the invention, a informative fusion device can include more or fewer components than those shown, or some components can be combined, or some components can be separated, or different arrangements of components can be used. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
Because the content of information interaction, execution process, and the like among the modules in the device is based on the same concept as the method embodiment of the present invention, specific content can be referred to the description in the method embodiment of the present invention, and is not described herein again.
The embodiment of the invention also provides electronic equipment which comprises a memory and a processor, wherein the memory is stored with a computer program, and when the processor executes the computer program, the fusion method of the intelligence information in any embodiment of the invention is realized.
The embodiment of the invention also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the processor is enabled to execute the fusion method of the intelligence information in any embodiment of the invention.
Specifically, a system or an apparatus equipped with a storage medium on which software program codes that realize the functions of any of the above-described embodiments are stored may be provided, and a computer (or a CPU or MPU) of the system or the apparatus is caused to read out and execute the program codes stored in the storage medium.
In this case, the program code itself read from the storage medium can realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.
Examples of the storage medium for supplying the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD + RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer by a communications network.
Further, it should be clear that the functions of any one of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform a part or all of the actual operations based on instructions of the program code.
Further, it is to be understood that the program code read out from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion module is caused to perform part or all of the actual operations based on instructions of the program code, thereby realizing the functions of any of the embodiments described above.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a process, method, article, or apparatus that comprises a list of elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for fusing information is characterized by comprising the following steps:
acquiring a plurality of pieces of information; each intelligence information comprises a name field, a basic attribute field and an association relation field;
based on a pre-constructed standard dictionary table, carrying out standard field conversion on a name field, a basic attribute field and an association relation field in each information message;
carrying out information fusion on the information with the same name field after the standard field is converted so as to generate target information through fusion;
and filtering the association relation in the target information based on a pre-constructed relation mode table to obtain standard information.
2. The method of claim 1, wherein the standard field transformation of the name field, the basic attribute field and the association relation field in each informative message based on the pre-constructed standard dictionary table comprises:
generating an original information table based on a name field, a basic attribute field and an incidence relation field in the information;
carrying out similarity analysis on each name field, each basic attribute field and each association relation field in the original information table and each standard field in a pre-constructed standard dictionary table to generate a mapping relation table;
and performing standard field conversion on the name field, the basic attribute field and the association relation field of each information in the original information table based on the mapping relation table.
3. The method of claim 2, wherein the similarity analysis is performed by any one of the following methods: cosine similarity, euclidean distance.
4. The method of claim 1, wherein the fusing the information with the same name field after converting the standard field to generate the target information comprises:
sorting the information with the same name field after the standard field is converted;
according to the descending order, aiming at each information after the first information, executing the following steps:
comparing the basic attribute field and the incidence relation field of the current information with the basic attribute field and the incidence relation field of the first information respectively;
if the current information has a basic attribute field and an association relation field which are different from the first information, the different basic attribute field and the association relation field are supplemented into the first information;
the supplemented first information is used as target information.
5. The method of claim 4, wherein sorting the intelligence information with the same name field after converting the standard field comprises:
acquiring the information source weight of the information with the same name field after the standard field is converted;
and sorting the information with the same name field after the standard field is converted according to the descending order of the information source weight.
6. The method of claim 5, further comprising, after sorting the intelligence messages having the same name field after converting the standard field in descending order of intelligence source weight:
when the information source weights of the information are the same, sorting is performed according to the generation time of the information.
7. The method according to any of claims 1-6, wherein the filtering the correlation in the target intelligence information based on a pre-constructed relationship schema table to obtain standard intelligence information comprises:
acquiring a pre-constructed relation mode table; wherein, the relation mode table contains the type of the association relation to be reserved for each type of target information;
and filtering the association relation in the target information to obtain standard information based on the type of the target information and the type of the association relation to be reserved for each type of target information in the relation mode table.
8. An information fusion device, comprising:
the acquisition unit is used for acquiring a plurality of pieces of information; each intelligence information comprises a name field, a basic attribute field and an association relation field;
the normalization unit is used for carrying out standard field conversion on the name field, the basic attribute field and the incidence relation field in each information based on a pre-constructed standard dictionary table;
the fusion unit is used for carrying out information fusion on the information with the same name field after the standard field is converted so as to generate target information through fusion;
and the filtering unit is used for filtering the incidence relation in the target information based on a pre-constructed relation mode table to obtain standard information.
9. An electronic device comprising a memory having stored therein a computer program and a processor that, when executing the computer program, implements the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when executed in a computer, causes the computer to carry out the method of any one of claims 1-7.
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