CN117336097A - Network information security management method and system based on big data - Google Patents
Network information security management method and system based on big data Download PDFInfo
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- H—ELECTRICITY
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- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/20—Network architectures or network communication protocols for network security for managing network security; network security policies in general
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- H—ELECTRICITY
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- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1425—Traffic logging, e.g. anomaly detection
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1433—Vulnerability analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
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Abstract
The invention provides a network information security management method and system based on big data, which relate to the field of network security management and comprise the following steps: determining the judgment scores of all network technology coverage data in all collected network information data under each risk influence factor; determining the weight of each risk influence factor based on all the judgment scores; determining a security influence value corresponding to each risk influence factor based on the weight information, and constructing a security representation matrix of each piece of network security technology coverage data based on all the security influence values; judging whether the corresponding network security technology coverage data is safe or not based on the security representation matrix, and obtaining a security detection result; and carrying out security management on all network security technology coverage data based on all security detection results. The invention solves the defect that the safety management operation is not actively carried out according to the network information data of the big data center, and carries out the safety management on the network information data of the big data center more predictively.
Description
Technical Field
The invention relates to the field of network security management, in particular to a network information security management method and system based on big data.
Background
At present, with the development and popularization of the internet, network information security has become a problem that must be paid attention to various fields of society. Traditional information management means cannot meet the information security requirements in the modern information age. Therefore, the network information security management method based on big data is gradually becoming a novel information management means.
However, the existing network information security management method based on big data only performs subsequent security management operation based on network protection information when being attacked, but does not actively perform security management operation according to network information data of the big data center, and is difficult to perform security management on the network information data in the big data center more efficiently and predictably. For example, publication number "CN114500020a", patent name "a network security management method based on big data", the method includes the following steps: acquiring first network protection information of a big data service communication terminal; updating the second network protection information of the big data platform according to the first network protection information: and executing network security management operation by adopting the updated second network protection information to complete the network security management flow. By applying the embodiment of the invention, the second network protection information corresponding to the big data platform is dynamically configured, so that the network security can be further ensured, and the occurrence of data leakage when the big data resource is acquired through the big data platform is reduced. However, the patent only passively performs subsequent security management operations based on network protection information when attacked, and does not actively perform security management operations according to network information data of a large data center.
Therefore, the invention provides a network information security management method and system based on big data.
Disclosure of Invention
The invention provides a network information security management method and system based on big data, which are used for obtaining network security technology coverage data in network information data, narrowing the range of the data needing network information security management, ensuring that the security management is more accurate, ensuring that the judgment score of the network security technology coverage data under each risk influence factor is more accurately obtained, quantifying the influence degree of each risk influence factor on the network security technology coverage data at the current moment according to the judgment score, quantifying the importance degree of each risk influence factor on all risk influence factors at the current moment according to the weight degree of each risk influence factor, constructing a security representation matrix of each network security technology coverage data according to the security influence value corresponding to each risk influence factor, ensuring that whether the security detection result of each network security technology coverage data needs security management is more accurately obtained according to the security representation matrix, ensuring that the network security technology coverage data needing security management of a big data center is more accurately and more efficiently isolated according to the security detection result, and realizing that the network security management data of the big data center is more efficiently foreseen according to the network information of the big data center.
The invention provides a network information security management method based on big data, which comprises the following steps:
s1: acquiring network information data of a large data center in real time, and determining the judgment score of all network security technology coverage data under each risk influence factor in all network information data acquired at the current moment based on a risk influence factor judgment model;
s2: determining the weight of each risk influence factor based on the judgment scores of all network security technology coverage data under each risk influence factor;
s3: determining a safety influence value corresponding to each risk influence factor based on the weight of each risk influence factor, and constructing a safety representation matrix of each network security technology coverage data at the current moment based on the safety influence values of all risk influence factors;
s4: judging whether each piece of network security technology coverage data is safe or not based on a security representation matrix of each piece of network security technology coverage data at the current moment, and obtaining a security detection result of each piece of network security technology coverage data;
s5: and carrying out security management on all network security technology coverage data of the big data center based on all security detection results to obtain security management results.
Preferably, the network information security management method based on big data, S1: network information data of a large data center are collected in real time, and the judgment score of all network security technology coverage data in all network information data collected at the current moment under each risk influence factor is determined based on a risk influence factor judgment model, wherein the method comprises the following steps:
S101: collecting network information data of a big data center at the current moment in real time, and extracting all network security technology coverage data at the current moment from all the collected network information data;
s102: and carrying out score judgment on all network security technology coverage data at the current moment based on a pre-trained risk influence factor judgment model and a plurality of preset risk influence factors to obtain judgment scores of all network security technology coverage data at the current moment under each risk influence factor, wherein the plurality of preset risk influence factors comprise information encryption measures, virus precaution measures, anti-hacking measures, security audit measures and access monitoring measures.
Preferably, the network information security management method based on big data extracts all network security technology coverage data at the current moment from all collected network information data, including:
detecting whether a data security technology exists on network information data acquired at the current moment in real time;
classifying the network information data into network security technology coverage data when detecting that the data security technology exists on the network information data;
and classifying the network information data as non-network security technology coverage data when the data security technology does not exist on the network information data.
Preferably, the network information security management method based on big data, S2: based on the judgment scores of all network security technology coverage data under each risk influence factor, determining the weight of each risk influence factor comprises the following steps:
acquiring judgment scores of all network security technology coverage data at the current moment under each risk influence factor;
summing the judgment scores of all network security technology coverage data under each risk influence factor to obtain the score sum of each risk factor;
the ratio of the sum of the scores of each risk influencing factor to the sum of the scores of all risk influencing factors is taken as the weight of each risk influencing factor.
Preferably, the network information security management method based on big data, S3: determining a security influence value corresponding to each risk influence factor based on the weight of each risk influence factor, and constructing a security representation matrix of each piece of network security technology coverage data at the current moment based on the security influence values of all risk influence factors, wherein the security representation matrix comprises the following components:
s301: taking the ratio of the weight of each risk influence factor to the sum of the weights of all risk influence factors as a safety influence value of each risk influence factor;
S302: defining ordinals of all network security technology coverage data at the current time;
s303: and constructing a security representation matrix of each piece of network security technology coverage data at the current moment based on the security influence values of all risk influence factors and the ordinal number of each piece of network security technology coverage data at the current moment.
Preferably, the network information security management method based on big data, S303: based on the security influence values of all risk influence factors and the ordinal number of each piece of network security technology coverage data at the current moment, a security representation matrix of each piece of network security technology coverage data at the current moment is constructed, and the method comprises the following steps:
determining the sum of the judgment scores of each piece of network security technology coverage data at the current moment under all risk influence factors, and constructing a security representation matrix of each piece of network security technology coverage data at the current moment based on the security influence values of all risk influence factors, the ordinal number of each piece of network security technology coverage data at the current moment and the sum of the judgment scores of each piece of network security technology coverage data under all risk influence factors, wherein the security representation matrix comprises the following components:
wherein S is i Covering a security representation matrix of data for the current network security technology with the time number of i, and i is E [1, n ]，Covering the sum of the judgment scores of the data under all risk influence factors for the network security technology with the current time number of i, wherein A is the security influence value of an information encryption measure, B is the security influence value of a virus precaution measure, C is the security influence value of a hacking prevention measure, D is the security influence value of a security audit measure, E is the security influence value of an access monitoring measure, alpha 11 The difference value alpha between the current time-series 1 network security technology coverage data and the current time-series 1 network security technology coverage data decision score under the information encryption measure 1n B, the difference value between the current network security technology coverage data with the time sequence number of 1 and the judgment score of the current network security technology coverage data with the time sequence number of n under the information encryption measure is b 11 B, the difference value between the current time-1 network security technology coverage data and the current time-1 network security technology coverage data decision score under the virus countermeasure 1n C, the difference value between the current network security technology coverage data with the time number of 1 and the judgment score of the current network security technology coverage data with the time number of n under the virus countermeasure 11 Determination of the current time 1 and current time 1 network security overlay data under anti-hacking measures The difference between the scores, c 1n D, the difference value between the current network security technology coverage data with the time sequence number of 1 and the judgment score of the current network security technology coverage data with the time sequence number of n under the anti-hacking measure 11 D, the difference value between the current network security technology coverage data with the time sequence number of 1 and the judgment score of the current network security technology coverage data with the time sequence number of 1 under the security audit measure is d 1n E, the difference value between the current network security technology coverage data with the time number of 1 and the judgment score of the current network security technology coverage data with the time number of n under the security audit measure is obtained 11 E, the difference value between the current network security technology coverage data with the time sequence number of 1 and the judgment score of the current network security technology coverage data with the time sequence number of 1 under the access monitoring measure 1n The difference value between the judgment scores of the current time number 1 network security technology coverage data and the current time number n network security technology coverage data under the access monitoring measures is obtained.
Preferably, the network information security management method based on big data, S4: judging whether each piece of network security technology coverage data is safe or not based on a security representation matrix of each piece of network security technology coverage data at the current moment, and obtaining a security detection result of each piece of network security technology coverage data, wherein the security detection result comprises the following steps:
S401: determining the safety degree of each piece of network security technology coverage data based on the safety representation matrix of each piece of network security technology coverage data at the current moment:
s402: and judging whether the safety degree of each piece of network security technology coverage data exceeds a preset value in real time, if so, judging that the corresponding piece of network security technology coverage data is unsafe as a safety detection result of the corresponding piece of network security technology coverage data, otherwise, judging that the corresponding piece of network security technology coverage data is safe as a safety detection result of the corresponding piece of network security technology coverage data.
Preferably, the network information security management method based on big data, S401: determining the security degree of each piece of network security technology coverage data based on the security representation matrix of each piece of network security technology coverage data at the current moment, including:
wherein Q is i The safety degree of the ith network security technology coverage data at the current moment is obtained, e is a natural logarithm, the value of e is 2.718, n is the total number of all network security technology coverage data at the current moment, delta is the maximum characteristic value of the safety representation matrix of the ith network security technology coverage data at the current moment,the rank of a security representation matrix covering data for the ith network security technology at the current moment, S i Covering a security representation matrix of data for a current time-ordered i network security technology, max (S i ) The security representation matrix of the data is covered for the current time instant i network security technology.
Preferably, the network information security management method based on big data, S5: based on all security detection results, performing security management on all network security technology coverage data of the big data center to obtain security management results, including:
acquiring a security detection result corresponding to each piece of network security technology coverage data at the current moment;
when the security detection result is judged to be that the corresponding stripe network security technology covers the data security, the corresponding security detection result is reserved:
when the security detection result is judged to be that the corresponding strip network security technology coverage data is not secure, the information isolation is carried out on the corresponding strip network security technology coverage data, and all the isolated strip network security technology coverage data are transmitted to a data center management background in real time.
Preferably, the big data based network information security management system is configured to perform any one of the big data based network information security management methods of embodiments 1 to 9, and includes:
the judging score acquisition module is used for acquiring network information data of the large data center in real time and determining judging scores of all network security technology coverage data under each risk influence factor in all network information data acquired at the current moment based on the risk influence factor judging model;
The weight degree module is used for determining the weight degree of each risk influence factor based on the judgment scores of all network security technology coverage data under each risk influence factor;
the safety representation matrix construction module is used for determining a safety influence value corresponding to each risk influence factor based on the weight of each risk influence factor, and constructing a safety representation matrix of each network security technology coverage data at the current moment based on the safety influence values of all the risk influence factors;
the judging module is used for judging whether each piece of network security technology coverage data is safe or not based on the security representation matrix of each piece of network security technology coverage data at the current moment, and obtaining a security detection result of each piece of network security technology coverage data;
and the security management module is used for carrying out security management on all network security technology coverage data of the big data center based on all security detection results to obtain security management results.
Compared with the prior art, the invention has the following beneficial effects: the network security technology coverage data in the network information data is obtained, the range of the data needing network information security management is reduced, the security management is more accurate, the judgment score of the network security technology coverage data under each risk influence factor is obtained more accurately, the influence degree of each risk influence factor on the network security technology coverage data at the current moment is quantified according to the judgment score, the importance degree of each risk influence factor on all risk influence factors at the current moment is quantified according to the weight degree of each risk influence factor, a security representation matrix of each network security technology coverage data is constructed according to the security influence value corresponding to each risk influence factor, the security detection result of whether each network security technology coverage data needs security management or not is obtained more accurately according to the security representation matrix, the network security technology coverage data needing security management of a large data center is isolated more accurately and more efficiently according to the security detection result, the network security management operation of the network information data of the large data center is actively performed, and the network security information data in the large data center is managed more efficiently and more predictably.
Additional features and advantages of the invention 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 invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a network information security management method based on big data in an embodiment of the invention;
fig. 2 is a specific flowchart of a flow step S3 in a network information security management method based on big data according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a network information security management system based on big data in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
the invention provides a network information security management method based on big data, referring to fig. 1, comprising the following steps:
s1: acquiring network information data of a large data center in real time, and determining the judgment score of all network security technology coverage data under each risk influence factor in all network information data acquired at the current moment based on a risk influence factor judgment model;
s2: determining the weight of each risk influence factor based on the judgment scores of all network security technology coverage data under each risk influence factor;
s3: determining a safety influence value corresponding to each risk influence factor based on the weight of each risk influence factor, and constructing a safety representation matrix of each network security technology coverage data at the current moment based on the safety influence values of all risk influence factors;
s4: judging whether each piece of network security technology coverage data is safe or not based on a security representation matrix of each piece of network security technology coverage data at the current moment, and obtaining a security detection result of each piece of network security technology coverage data;
s5: and carrying out security management on all network security technology coverage data of the big data center based on all security detection results to obtain security management results.
In this embodiment, the network information data is user data or user browsing data stored in the big data center and obtained based on network and big data technology, and the network information data includes multiple types of data including structured data, semi-structured data and unstructured data. (structured data refers to data stored according to fixed formats and rules, such as table data in a database; semi-structured data refers to data which has a certain structure but is not stored according to fixed formats and rules, such as XML, JSON, etc., unstructured data refers to data without fixed formats and rules, such as text, audio, video, etc.), and a big data center is a data system for acquiring and storing corresponding network information data based on big data technology.
In this embodiment, the risk influencing factors are preset several main measures that influence the security of the network information, including an information encryption measure, a virus countermeasure, an anti-hacking measure, a security audit measure, and an access monitoring measure, where the above measures are related to the relevant attribute parameters such as the intensity of the corresponding program set in the storage document storing the corresponding network information data, for example, the information encryption measure is related to the intensity of the information encryption program set in the storage document storing the corresponding network information data.
In this embodiment, the risk impact factor determination model is a risk impact factor determination model trained by taking a large amount of network information data and the determination score thereof under each risk impact factor as a training sample in advance, and the risk impact factor determination model can determine the determination score thereof under each risk impact factor according to the network information data.
In this embodiment, the network security technology coverage data is a part of network information data in which a data security technology (a data access authority setting, information encryption, etc. technology) exists on all network information data.
In this embodiment, the decision score is a score value (the decision score is 1-10 points) of the network security technology coverage data under each risk influence factor determined based on the risk influence factor decision model.
In this embodiment, the weight of each risk influencing factor (representing the extent of influence of each risk influencing factor on the network security technology coverage data at the current moment) is the ratio of the judgment score sum of all network security technology coverage data under each risk influencing factor to the score sum of all risk influencing factors.
In this embodiment, the security impact value corresponding to each risk impact factor (the relative importance degree of the impact degree of each risk impact factor on the network security technology coverage data in all risk impact factors and the relative impact degree of the risk impact factor on the security of the corresponding network security technology coverage data in the representation of all risk impact factors at the current moment) is the ratio of the weight of each risk impact factor to the sum of the weights of all risk impact factors.
In this embodiment, the security representation matrix is a matrix that is constructed corresponding to each piece of network security technology coverage data at the current moment based on the difference between the security impact values of all risk impact factors and the decision scores of two pieces of network security technology coverage data in all network security technology coverage data, and the security representation matrix is a matrix for representing the security of the network security technology coverage data.
In this embodiment, the security detection result is a determination result that, based on the security representation matrix of each piece of the network security technology coverage data at the current time, the corresponding security degree is obtained, and whether each piece of the network security technology coverage data is safe or not is determined according to the security degree (whether the security degree exceeds a preset value, whether the security degree exceeds the preset value, and otherwise, whether the corresponding piece of the network security technology coverage data is safe or not is determined).
In the embodiment, the security management performs information isolation on the corresponding strip network security technology coverage data, and transmits all the isolated network security technology coverage data to the data center management background in real time.
In this embodiment, the security management result is a result obtained after security management is performed on all network technology coverage data.
The beneficial effects of the technology are as follows: the network security technology coverage data in the network information data is obtained, the range of the data needing network information security management is reduced, the security management is more accurate, the judgment score of the network security technology coverage data under each risk influence factor is obtained more accurately, the influence degree of each risk influence factor on the network security technology coverage data at the current moment is quantified according to the judgment score, the importance degree of each risk influence factor on all risk influence factors at the current moment is quantified according to the weight degree of each risk influence factor, a security representation matrix of each network security technology coverage data is constructed according to the security influence value corresponding to each risk influence factor, the security detection result of whether each network security technology coverage data needs security management or not is obtained more accurately according to the security representation matrix, the network security technology coverage data needing security management of a large data center is isolated more accurately and more efficiently according to the security detection result, the network security management operation of the network information data of the large data center is actively performed, and the network security information data in the large data center is managed more efficiently and more predictably.
Example 2:
based on the embodiment 1, the network information security management method based on big data, S1: network information data of a large data center are collected in real time, and the judgment score of all network security technology coverage data in all network information data collected at the current moment under each risk influence factor is determined based on a risk influence factor judgment model, wherein the method comprises the following steps:
s101: collecting network information data of a big data center at the current moment in real time, and extracting all network security technology coverage data at the current moment from all the collected network information data;
s102: and carrying out score judgment on all network security technology coverage data at the current moment based on a pre-trained risk influence factor judgment model and a plurality of preset risk influence factors to obtain judgment scores of all network security technology coverage data at the current moment under each risk influence factor, wherein the plurality of preset risk influence factors comprise information encryption measures, virus precaution measures, anti-hacking measures, security audit measures and access monitoring measures.
The beneficial effects of the technology are as follows: the network information data of the large data center is obtained at the first moment, the network security technology coverage data in the network information data is obtained, the range of the data needing network information security management is reduced, the security management is more accurate, and the judgment score of the network security technology coverage data under each risk influence factor is obtained more accurately.
Example 3:
based on embodiment 2, the network information security management method based on big data extracts all network security technology coverage data at the current moment from all collected network information data, including:
detecting whether a data security technology exists on network information data acquired at the current moment in real time;
classifying the network information data into network security technology coverage data when detecting that the data security technology exists on the network information data;
and classifying the network information data as non-network security technology coverage data when the data security technology does not exist on the network information data.
In this embodiment, the data security technology is a data security technology set on a storage location or a document storing corresponding network information data, and includes a data access authority setting technology, an information encryption technology, and the like.
In this embodiment, the detection of the presence of the data security technology on the network information data is detected by the acquired determination of the relevant data storing the storage location or document corresponding to the network information technology.
In this embodiment, the non-network security technology coverage data is a part of network information data in which a data security technology (a technology of setting data access authority, encrypting information, etc.) does not exist in a storage document or a location where the corresponding network information data is stored in all the network information data.
The beneficial effects of the technology are as follows: and a part of network information data which may need subsequent security management in all network information data is obtained more accurately.
Example 4:
based on the embodiment 1, the network information security management method based on big data, S2: based on the judgment scores of all network security technology coverage data under each risk influence factor, determining the weight of each risk influence factor comprises the following steps:
acquiring judgment scores of all network security technology coverage data at the current moment under each risk influence factor;
summing the judgment scores of all network security technology coverage data under each risk influence factor to obtain the score sum of each risk factor;
the ratio of the sum of the scores of each risk influencing factor to the sum of the scores of all risk influencing factors is taken as the weight of each risk influencing factor.
In this embodiment, the sum of the scores is the sum of the scores of the decision scores for all web technologies coverage data under each risk influencing factor.
The beneficial effects of the technology are as follows: and quantifying the influence degree of each risk influence factor on the network security technology coverage data at the current moment according to the judgment score, wherein the influence degree is more accurate.
Example 5:
based on the embodiment 1, the network information security management method based on big data, S3: determining a security influence value corresponding to each risk influence factor based on the weight of each risk influence factor, and constructing a security representation matrix of each piece of network security technology coverage data at the current moment based on the security influence values of all risk influence factors, referring to fig. 2, including:
s301: taking the ratio of the weight of each risk influence factor to the sum of the weights of all risk influence factors as a safety influence value of each risk influence factor;
s302: defining ordinals of all network security technology coverage data at the current time;
s303: and constructing a security representation matrix of each piece of network security technology coverage data at the current moment based on the security influence values of all risk influence factors and the ordinal number of each piece of network security technology coverage data at the current moment.
In this embodiment, the ordinal numbers of all the network technology coverage data at the current time are defined according to a preset rule, and specifically, for example, the ordinal numbers can be defined according to the principle that the acquisition time of the network technology coverage data is from front to back.
The beneficial effects of the technology are as follows: and quantifying the relative duty ratio of the influence degree of each risk influence factor on the network security technology coverage data in all risk influence factors (namely the relative influence degree of each risk influence factor on the network security technology coverage data) according to the weight degree of each risk influence factor, and constructing a security representation matrix of each network security technology coverage data according to the security influence value corresponding to each risk influence factor.
Example 6:
based on embodiment 5, the network information security management method based on big data, S303: based on the security influence values of all risk influence factors and the ordinal number of each piece of network security technology coverage data at the current moment, a security representation matrix of each piece of network security technology coverage data at the current moment is constructed, and the method comprises the following steps:
determining the sum of the judgment scores of each piece of network security technology coverage data at the current moment under all risk influence factors, and constructing a security representation matrix of each piece of network security technology coverage data at the current moment based on the security influence values of all risk influence factors, the ordinal number of each piece of network security technology coverage data at the current moment and the sum of the judgment scores of each piece of network security technology coverage data under all risk influence factors, wherein the security representation matrix comprises the following components:
wherein S is i Covering a security representation matrix of data for the current network security technology with the time number of i, and i is E [1, n]，Covering the sum of the judgment scores of the data under all risk influence factors for the network security technology with the current time number of i, wherein A is the security influence value of an information encryption measure, B is the security influence value of a virus precaution measure, C is the security influence value of a hacking prevention measure, D is the security influence value of a security audit measure, E is the security influence value of an access monitoring measure, alpha 11 The difference value alpha between the current time-series 1 network security technology coverage data and the current time-series 1 network security technology coverage data decision score under the information encryption measure 1n B, the difference value between the current network security technology coverage data with the time sequence number of 1 and the judgment score of the current network security technology coverage data with the time sequence number of n under the information encryption measure is b 11 B, the difference value between the current time-1 network security technology coverage data and the current time-1 network security technology coverage data decision score under the virus countermeasure 1n C, the difference value between the current network security technology coverage data with the time number of 1 and the judgment score of the current network security technology coverage data with the time number of n under the virus countermeasure 11 C, the difference value between the current time-series 1 network security technology coverage data and the current time-series 1 network security technology coverage data decision score under the anti-hacking measure 1n For a current time ordinal number of 1D) the difference between the network security technology coverage data and the decision score of the network security technology coverage data with the current time sequence of n under the anti-hacking measure 11 D, the difference value between the current network security technology coverage data with the time sequence number of 1 and the judgment score of the current network security technology coverage data with the time sequence number of 1 under the security audit measure is d 1n E, the difference value between the current network security technology coverage data with the time number of 1 and the judgment score of the current network security technology coverage data with the time number of n under the security audit measure is obtained 11 E, the difference value between the current network security technology coverage data with the time sequence number of 1 and the judgment score of the current network security technology coverage data with the time sequence number of 1 under the access monitoring measure 1n The difference value between the judgment scores of the current time number 1 network security technology coverage data and the current time number n network security technology coverage data under the access monitoring measures is obtained.
In this embodiment, the difference between the judgment scores is the difference between the judgment scores of two pieces of network security technology coverage data under a single measure (for example, under an information encryption measure, under a virus countermeasure, under an anti-hacking measure, etc.).
The beneficial effects of the technology are as follows: and constructing a matrix corresponding to each piece of network security technology coverage data at the current moment according to the difference value between the security influence value of all risk influence factors and the judgment scores of two pieces of network security technology coverage data in all network security technology coverage data, so as to realize visual matrix representation of the security of the network security technology coverage data.
Example 7:
based on the embodiment 1, the network information security management method based on big data, S4: judging whether each piece of network security technology coverage data is safe or not based on a security representation matrix of each piece of network security technology coverage data at the current moment, and obtaining a security detection result of each piece of network security technology coverage data, wherein the security detection result comprises the following steps:
s401: determining the safety degree of each piece of network security technology coverage data based on the safety representation matrix of each piece of network security technology coverage data at the current moment;
s402: and judging whether the safety degree of each piece of network security technology coverage data exceeds a preset value in real time, if so, judging that the corresponding piece of network security technology coverage data is unsafe as a safety detection result of the corresponding piece of network security technology coverage data, otherwise, judging that the corresponding piece of network security technology coverage data is safe as a safety detection result of the corresponding piece of network security technology coverage data.
In this embodiment, the security represents the security degree of the data information of the data covered by the network security technology, and the smaller the security degree is, the less secure the data information of the data covered by the network security technology is.
In this embodiment, the preset value is a preset judgment threshold value for judging whether the security degree according to which the network security technology covers the data is safe or not, and the preset value is a security degree threshold value which is obtained according to the performance training of a large number of network security technologies with different security degrees in the data security later or is obtained according to manual calibration and can ensure that the security degree of the data meets the requirement.
In this embodiment, the network security technology coverage data is not secure, and the network security technology coverage data may have risks of data leakage and loss in the future, and needs to be subjected to subsequent security management.
In the embodiment, the network security technology coverage data security is that the network security technology coverage data is free from risks of data leakage and loss in the future, and subsequent security management is not needed.
The beneficial effects of the technology are as follows: and judging whether each piece of network security technology coverage data needs security management or not according to the security representation matrix more accurately, and obtaining a corresponding security detection result.
Example 8:
based on embodiment 7, the network information security management method based on big data, S401: determining the security degree of each piece of network security technology coverage data based on the security representation matrix of each piece of network security technology coverage data at the current moment, including:
wherein Q is i The safety degree of the ith network security technology coverage data at the current moment is obtained, e is a natural logarithm, the value of e is 2.718, n is the total number of all network security technology coverage data at the current moment, delta is the maximum characteristic value of the safety representation matrix of the ith network security technology coverage data at the current moment, The rank of a security representation matrix covering data for the ith network security technology at the current moment, S i Covering a security representation matrix of data for a current time-ordered i network security technology, max (S i ) The security representation matrix of the data is covered for the current time instant i network security technology.
The beneficial effects of the technology are as follows: and precisely calculating and obtaining the safety degree of each piece of network security technology coverage data.
Example 9:
based on the embodiment 1, the network information security management method based on big data, S5: based on all security detection results, performing security management on all network security technology coverage data of the big data center to obtain security management results, including:
acquiring a security detection result corresponding to each piece of network security technology coverage data at the current moment;
when the security detection result is judged to be the security of the corresponding data covered by the bar network security technology, the corresponding security detection result is reserved (namely, the information isolation is not carried out on the data covered by the corresponding bar network security technology);
when the security detection result is judged to be that the corresponding strip network security technology coverage data is not secure, the information isolation is carried out on the corresponding strip network security technology coverage data, and all the isolated strip network security technology coverage data are transmitted to a data center management background in real time.
In this embodiment, the information isolation is to perform multiple auditing and monitoring measures on the network security technology coverage data, so as to ensure that all the network security technology coverage data with the information isolation are closely checked and controlled.
In this embodiment, the data center management background is a data management background center of a large data center.
The beneficial effects of the technology are as follows: according to the security detection result, the network security technology coverage data needing to be subjected to security management in the large data center is subjected to more accurate and more efficient information isolation.
Example 10:
the present invention provides a network information security management system based on big data, for executing any one of the network information security management methods based on big data in embodiments 1 to 9, referring to fig. 3, comprising:
the judging score acquisition module is used for acquiring network information data of the large data center in real time and determining judging scores of all network security technology coverage data under each risk influence factor in all network information data acquired at the current moment based on the risk influence factor judging model;
the weight degree module is used for determining the weight degree of each risk influence factor based on the judgment scores of all network security technology coverage data under each risk influence factor;
The safety representation matrix construction module is used for determining a safety influence value corresponding to each risk influence factor based on the weight of each risk influence factor, and constructing a safety representation matrix of each network security technology coverage data at the current moment based on the safety influence values of all the risk influence factors;
the judging module is used for judging whether each piece of network security technology coverage data is safe or not based on the security representation matrix of each piece of network security technology coverage data at the current moment, and obtaining a security detection result of each piece of network security technology coverage data;
and the security management module is used for carrying out security management on all network security technology coverage data of the big data center based on all security detection results to obtain security management results.
The beneficial effects of the technology are as follows: the network security technology coverage data in the network information data is obtained, the range of the data needing network information security management is reduced, the security management is more accurate, the judgment score of the network security technology coverage data under each risk influence factor is obtained more accurately, the influence degree of each risk influence factor on the network security technology coverage data at the current moment is quantified according to the judgment score, the importance degree of each risk influence factor on all risk influence factors at the current moment is quantified according to the weight degree of each risk influence factor, a security representation matrix of each network security technology coverage data is constructed according to the security influence value corresponding to each risk influence factor, the security detection result of whether each network security technology coverage data needs security management or not is obtained more accurately according to the security representation matrix, the network security technology coverage data needing security management of a large data center is isolated more accurately and more efficiently according to the security detection result, the network security management operation of the network information data of the large data center is actively performed, and the network security information data in the large data center is managed more efficiently and more predictably.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The network information security management method based on big data is characterized by comprising the following steps:
s1: acquiring network information data of a large data center in real time, and determining the judgment score of all network security technology coverage data under each risk influence factor in all network information data acquired at the current moment based on a risk influence factor judgment model;
s2: determining the weight of each risk influence factor based on the judgment scores of all network security technology coverage data under each risk influence factor;
s3: determining a safety influence value corresponding to each risk influence factor based on the weight of each risk influence factor, and constructing a safety representation matrix of each network security technology coverage data at the current moment based on the safety influence values of all risk influence factors;
s4: judging whether each piece of network security technology coverage data is safe or not based on a security representation matrix of each piece of network security technology coverage data at the current moment, and obtaining a security detection result of each piece of network security technology coverage data;
S5: and carrying out security management on all network security technology coverage data of the big data center based on all security detection results to obtain security management results.
2. The network information security management method based on big data as claimed in claim 1, wherein S1: network information data of a large data center are collected in real time, and the judgment score of all network security technology coverage data in all network information data collected at the current moment under each risk influence factor is determined based on a risk influence factor judgment model, wherein the method comprises the following steps:
s101: collecting network information data of a big data center at the current moment in real time, and extracting all network security technology coverage data at the current moment from all the collected network information data;
s102: and carrying out score judgment on all network security technology coverage data at the current moment based on a pre-trained risk influence factor judgment model and a plurality of preset risk influence factors to obtain judgment scores of all network security technology coverage data at the current moment under each risk influence factor, wherein the plurality of preset risk influence factors comprise information encryption measures, virus precaution measures, anti-hacking measures, security audit measures and access monitoring measures.
3. The network information security management method based on big data according to claim 2, wherein extracting all network security technology coverage data at the current moment from all collected network information data comprises:
detecting whether a data security technology exists on network information data acquired at the current moment in real time;
classifying the network information data into network security technology coverage data when detecting that the data security technology exists on the network information data;
and classifying the network information data as non-network security technology coverage data when the data security technology does not exist on the network information data.
4. The network information security management method based on big data as claimed in claim 1, wherein S2: based on the judgment scores of all network security technology coverage data under each risk influence factor, determining the weight of each risk influence factor comprises the following steps:
acquiring judgment scores of all network security technology coverage data at the current moment under each risk influence factor;
summing the judgment scores of all network security technology coverage data under each risk influence factor to obtain the score sum of each risk factor;
The ratio of the sum of the scores of each risk influencing factor to the sum of the scores of all risk influencing factors is taken as the weight of each risk influencing factor.
5. The network information security management method based on big data as claimed in claim 1, wherein S3: determining a security influence value corresponding to each risk influence factor based on the weight of each risk influence factor, and constructing a security representation matrix of each piece of network security technology coverage data at the current moment based on the security influence values of all risk influence factors, wherein the security representation matrix comprises the following components:
s301: taking the ratio of the weight of each risk influence factor to the sum of the weights of all risk influence factors as a safety influence value of each risk influence factor;
s302: defining ordinals of all network security technology coverage data at the current time;
s303: and constructing a security representation matrix of each piece of network security technology coverage data at the current moment based on the security influence values of all risk influence factors and the ordinal number of each piece of network security technology coverage data at the current moment.
6. The method for security management of network information based on big data according to claim 5, wherein S303: based on the security influence values of all risk influence factors and the ordinal number of each piece of network security technology coverage data at the current moment, a security representation matrix of each piece of network security technology coverage data at the current moment is constructed, and the method comprises the following steps:
Determining the sum of the judgment scores of each piece of network security technology coverage data at the current moment under all risk influence factors, and constructing a security representation matrix of each piece of network security technology coverage data at the current moment based on the security influence values of all risk influence factors, the ordinal number of each piece of network security technology coverage data at the current moment and the sum of the judgment scores of each piece of network security technology coverage data under all risk influence factors, wherein the security representation matrix comprises the following components:
wherein S is i Covering a security representation matrix of data for the current network security technology with the time number of i, and i is E [1, n]，Covering the sum of the judgment scores of the data under all risk influence factors for the network security technology with the current time number of i, wherein A is the security influence value of an information encryption measure, B is the security influence value of a virus precaution measure, C is the security influence value of a hacking prevention measure, D is the security influence value of a security audit measure, E is the security influence value of an access monitoring measure, alpha 11 The difference value alpha between the current time-series 1 network security technology coverage data and the current time-series 1 network security technology coverage data decision score under the information encryption measure 1n B, the difference value between the current network security technology coverage data with the time sequence number of 1 and the judgment score of the current network security technology coverage data with the time sequence number of n under the information encryption measure is b 11 Network security technology coverage data with current time sequence number of 1 and network security technology coverage data with current time sequence number of 1 are recorded in the networkDifference between judgment scores under virus countermeasure, b 1n C, the difference value between the current network security technology coverage data with the time number of 1 and the judgment score of the current network security technology coverage data with the time number of n under the virus countermeasure 11 C, the difference value between the current time-series 1 network security technology coverage data and the current time-series 1 network security technology coverage data decision score under the anti-hacking measure 1n D, the difference value between the current network security technology coverage data with the time sequence number of 1 and the judgment score of the current network security technology coverage data with the time sequence number of n under the anti-hacking measure 11 D, the difference value between the current network security technology coverage data with the time sequence number of 1 and the judgment score of the current network security technology coverage data with the time sequence number of 1 under the security audit measure is d 1n E, the difference value between the current network security technology coverage data with the time number of 1 and the judgment score of the current network security technology coverage data with the time number of n under the security audit measure is obtained 11 E, the difference value between the current network security technology coverage data with the time sequence number of 1 and the judgment score of the current network security technology coverage data with the time sequence number of 1 under the access monitoring measure 1n The difference value between the judgment scores of the current time number 1 network security technology coverage data and the current time number n network security technology coverage data under the access monitoring measures is obtained.
7. The method for managing network information security based on big data according to claim 1, wherein S4: judging whether each piece of network security technology coverage data is safe or not based on a security representation matrix of each piece of network security technology coverage data at the current moment, and obtaining a security detection result of each piece of network security technology coverage data, wherein the security detection result comprises the following steps:
s401: determining the safety degree of each piece of network security technology coverage data based on the safety representation matrix of each piece of network security technology coverage data at the current moment;
s402: and judging whether the safety degree of each piece of network security technology coverage data exceeds a preset value in real time, if so, judging that the corresponding piece of network security technology coverage data is unsafe as a safety detection result of the corresponding piece of network security technology coverage data, otherwise, judging that the corresponding piece of network security technology coverage data is safe as a safety detection result of the corresponding piece of network security technology coverage data.
8. The method for managing security of network information based on big data according to claim 1, wherein S401: determining the security degree of each piece of network security technology coverage data based on the security representation matrix of each piece of network security technology coverage data at the current moment, including:
Wherein Q is i The safety degree of the ith network security technology coverage data at the current moment is obtained, e is a natural logarithm, the value of e is 2.718, n is the total number of all network security technology coverage data at the current moment, delta is the maximum characteristic value of the safety representation matrix of the ith network security technology coverage data at the current moment,the rank of a security representation matrix covering data for the ith network security technology at the current moment, S i Covering a security representation matrix of data for a current time-ordered i network security technology, max (S i ) The security representation matrix of the data is covered for the current time instant i network security technology.
9. The method for managing network information security based on big data according to claim 1, wherein S5: based on all security detection results, performing security management on all network security technology coverage data of the big data center to obtain security management results, including:
acquiring a security detection result corresponding to each piece of network security technology coverage data at the current moment;
when the security detection result is judged to be the data security covered by the corresponding strip network security technology, the corresponding security detection result is reserved;
when the security detection result is judged to be that the corresponding strip network security technology coverage data is not secure, the information isolation is carried out on the corresponding strip network security technology coverage data, and all the isolated strip network security technology coverage data are transmitted to a data center management background in real time.
10. A big data based network information security management system for performing the big data based network information security management method of any of claims 1 to 9, comprising:
the judging score acquisition module is used for acquiring network information data of the large data center in real time and determining judging scores of all network security technology coverage data under each risk influence factor in all network information data acquired at the current moment based on the risk influence factor judging model;
the weight degree module is used for determining the weight degree of each risk influence factor based on the judgment scores of all network security technology coverage data under each risk influence factor;
the safety representation matrix construction module is used for determining a safety influence value corresponding to each risk influence factor based on the weight of each risk influence factor, and constructing a safety representation matrix of each network security technology coverage data at the current moment based on the safety influence values of all the risk influence factors;
the judging module is used for judging whether each piece of network security technology coverage data is safe or not based on the security representation matrix of each piece of network security technology coverage data at the current moment, and obtaining a security detection result of each piece of network security technology coverage data;
And the security management module is used for carrying out security management on all network security technology coverage data of the big data center based on all security detection results to obtain security management results.
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