CN115052171A - Network security monitoring data encryption system - Google Patents
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- CN115052171A CN115052171A CN202210645762.1A CN202210645762A CN115052171A CN 115052171 A CN115052171 A CN 115052171A CN 202210645762 A CN202210645762 A CN 202210645762A CN 115052171 A CN115052171 A CN 115052171A
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- 238000012545 processing Methods 0.000 claims abstract description 29
- 230000011218 segmentation Effects 0.000 claims abstract description 23
- 230000006698 induction Effects 0.000 claims abstract description 6
- 239000013598 vector Substances 0.000 claims description 30
- 238000000034 method Methods 0.000 claims description 20
- 238000012795 verification Methods 0.000 claims description 19
- 238000012937 correction Methods 0.000 claims description 6
- 238000006467 substitution reaction Methods 0.000 claims description 6
- 230000003139 buffering effect Effects 0.000 claims description 4
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/234—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
- H04N21/2347—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving video stream encryption
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
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- G06T2207/10016—Video; Image sequence
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Abstract
The invention discloses a network security monitoring data encryption system, which belongs to the technical field of network security and comprises an identification and segmentation module, an encryption module, a database and a server; performing key data identification through an identification and segmentation module, and segmenting corresponding monitoring video data to obtain segmented video segments; the encryption module is used for encrypting the monitoring data, acquiring video monitoring data, dividing the acquired video monitoring data into a normal video segment and a segmentation video segment, and acquiring target encryption data of the normal video segment; performing induction processing on the segmented video segments to obtain an induced video, splicing the induced video with the normal video segments to obtain a video to be encrypted, and encrypting the video to be encrypted through target encryption data to obtain monitoring encrypted video data; and setting unordered encrypted data of the divided video segments, and processing the divided video segments through the set unordered encrypted data to obtain encrypted processing data and recorded data.
Description
Technical Field
The invention belongs to the technical field of network security, and particularly relates to a network security monitoring data encryption system.
Background
With the rapid development and popularization of computer technology, video monitoring is widely applied to various industries, and networking, high-definition and intellectualization are gradually realized. Although the development prospect of video monitoring is generally seen, in practical application, video monitoring data are used as privacy data, and irrelevant people cannot be allowed to watch the video monitoring data under no special condition.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides a network security monitoring data encryption system.
The purpose of the invention can be realized by the following technical scheme:
the network security monitoring data encryption system comprises an identification and segmentation module, an encryption module, a database and a server;
performing key data identification through an identification and segmentation module, and segmenting corresponding monitoring video data to obtain segmented video segments;
the encryption module is used for encrypting the monitoring data, acquiring video monitoring data, dividing the acquired video monitoring data into a normal video segment and a segmentation video segment, and acquiring target encryption data of the normal video segment; performing induction processing on the segmented video segments to obtain an induced video, splicing the induced video with the normal video segments to obtain a video to be encrypted, and encrypting the video to be encrypted through target encryption data to obtain monitoring encrypted video data;
setting unordered encrypted data of the divided video segments, processing the divided video segments through the set unordered encrypted data to obtain encrypted processing data and recorded data, and sending the encrypted processing data to a database for storage; setting an encryption substitution model, setting verification data corresponding to the recording data, and inputting the recording data and the verification data into the encryption substitution model; the encryption replacement model is used for recovering the segmented video segments and replacing the induced video in the video to be encrypted.
Further, the working method for identifying the segmentation module comprises the following steps:
identifying an industry field corresponding to the monitoring data, matching a corresponding keyword set and corresponding example data according to the identified industry field, carrying out encryption segment phrase combination to obtain example data corresponding to an encryption segment phrase, marking the example data as target segment data, setting corresponding feature identification data according to the target segment data, setting corresponding buffer time according to the obtained feature identification data, and establishing an identification segmentation model according to the buffer time and the feature identification data;
and acquiring monitoring video data in real time, and identifying and segmenting the monitoring video data through an identification and segmentation model to obtain corresponding segmented video segments.
Further, the method of matching the corresponding keyword set and the corresponding example data according to the identified industry domain includes:
setting an application field, acquiring safety keywords of the application field, establishing a search formula, performing data search according to the established search formula to acquire field search data, setting corresponding example data according to the acquired field search data, associating the example data with safety keyword combinations corresponding to the search formula, integrating the safety keywords in the application field into a keyword set, establishing a field matching library, acquiring an industry field needing to be matched, inputting the industry field into the field matching library for matching, and acquiring the corresponding keyword set, the safety keyword combinations and the corresponding example data.
Further, the method for setting the corresponding buffering time according to the obtained feature identification data comprises the following steps:
and acquiring classification items corresponding to the feature identification data, matching corresponding initial values, marking the classification items as CS, setting corresponding buffer correction values according to the feature identification data, marking the buffer correction values as HC, matching corresponding division coefficients according to the acquired classification items, marking the division coefficients as alpha, and calculating buffer time according to a time formula.
Furthermore, the time formula is Tc ═ α × (b1 × CS + b2 × HC), where b1 and b2 are both proportional coefficients and have a value range of 0< b1 ≦ 1 and 0< b2 ≦ 1.
Further, the method for acquiring the target encrypted data of the normal video segment comprises the following steps:
acquiring video encryption data, setting encryption characteristic data of each video encryption data, and converting the set encryption characteristic data into encryption characteristic vectors; setting a standard requirement template, filling options according to the set standard requirement template by a manager to obtain target encryption requirement data, converting the target encryption requirement data into requirement vectors, calculating matching values between the requirement vectors and the encryption characteristic vectors, and selecting corresponding video encryption data as target encryption data according to the calculated matching values.
Further, the method of calculating the matching value between the demand vector and each of the encrypted feature vectors includes:
marking the demand vector as XQ ═ (P1, P2, Pi, … …, Pn), wherein i ═ 1, 2, … …, n is a positive integer; marking the encrypted feature vector as TZ ═ (H1, H2, Hi, … …, Hn), obtaining the weight coefficient beta i, and obtaining the weight coefficient beta i according to the formula
A match value between the demand vector and the encrypted feature vector is calculated.
Further, the decryption method of the monitoring encrypted video data comprises the following steps:
decrypting the monitoring encrypted video data through the target encrypted data to obtain a video to be encrypted; and carrying out information verification of the encryption replacement model, carrying out no operation when the verification fails, identifying corresponding recorded data when the verification succeeds, acquiring corresponding encryption processing data from the database, recovering the encryption processing data into a segmented video segment by the encryption replacement model, and replacing the induced video in the video to be encrypted.
Compared with the prior art, the invention has the beneficial effects that: through the mutual cooperation among the identification segmentation module, the encryption module and the database, the video monitoring data are segmented and encrypted differentially, the safety of the video monitoring data is improved from multiple aspects, the induction processing and replacement of key data are carried out under certain application environments, the safety of the encrypted data is further improved, the encrypted data is prevented from being decoded, meanwhile, the operation decryption of specified equipment is realized for certain special places, and the encryption and decryption of irrelevant personnel are avoided.
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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 described below, and it is obvious that the drawings in the following description are only 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 schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the network security monitoring data encryption system includes an identification partitioning module, an encryption module, a database and a server;
the identification and segmentation module is used for identifying key data and segmenting corresponding monitoring video data, and the specific method comprises the following steps:
identifying an industry field corresponding to the monitoring data, matching a corresponding keyword set and corresponding example data according to the identified industry field, carrying out encryption segment phrase combination to obtain example data corresponding to the encryption segment phrase, marking the example data as target segment data, setting corresponding feature identification data according to the target segment data, and setting corresponding buffer time according to the obtained feature identification data, wherein the buffer time is that when the feature identification data is identified, the buffer time is divided into a forward buffer time length at the beginning and a backward buffer time length at the end; establishing an identification segmentation model according to the buffer time and the feature identification data;
and acquiring monitoring video data in real time, and identifying and segmenting the monitoring video data through an identification and segmentation model to obtain corresponding segmented video segments.
The encryption segment phrase is to select the monitoring data segment to be encrypted according to the keyword set and the corresponding example data according to the requirement of the corresponding user, and further to set the corresponding encryption segment phrase.
The feature identification data is to identify the motion and data that need to be encrypted in the monitoring video data, such as the motion when inputting a password, the motion and picture for displaying a payment code, etc., and can be set by adopting the existing data model, or manually set the corresponding feature identification data for each sample data to perform matching.
The identification segmentation model is established based on a CNN network or a DNN network, corresponding characteristic identification data are identified in the surveillance video, the beginning and the end of the corresponding surveillance video are determined, and then the segmented video segments are obtained according to the segmentation of the surveillance video according to the buffering time.
The method of matching a corresponding set of keywords and corresponding sample data according to an identified industry domain includes:
setting the application field of the application, namely the application can be used in which industry fields, acquiring safety keywords of the application field, establishing a search formula, performing data search according to the established search formula to obtain field search data, setting corresponding example data according to the obtained field search data, associating the example data with safety keyword combinations corresponding to the search formula, integrating the safety keywords in the application field into a keyword set, establishing a field matching library, acquiring the industry fields needing to be matched, inputting the industry fields into the field matching library for matching, and acquiring the corresponding keyword set, the safety keyword combinations and the corresponding example data.
The method for setting the corresponding buffering time according to the obtained feature identification data comprises the following steps:
the method comprises the steps of obtaining classification items corresponding to feature identification data, matching corresponding initial values, marking the classification items as CS, setting corresponding buffer correction values according to the feature identification data, marking the buffer correction values as HC, matching corresponding demarcation coefficients according to the obtained classification items, marking the classification items as alpha, and calculating buffer time according to a formula Tc (alpha x) (b1 x CS + b2 x HC), wherein b1 and b2 are proportional coefficients, the value range is 0< b1 is less than or equal to 1, and 0< b2 is less than or equal to 1.
The method for matching the initial value according to the classification item comprises the following steps: and establishing a corresponding classification item initial value matching table by an expert group in advance, and obtaining the classification item after matching, wherein the classification item refers to which item is specifically input, such as a counter password, an ATM password and the like. The buffer correction value is set by an expert group mainly according to the data such as the recognition efficiency, the action span and the like of the corresponding characteristic recognition data. The boundary coefficient is set according to the corresponding beginning identification or ending identification of the classification item, namely, the boundary coefficient corresponding to the beginning and the boundary coefficient corresponding to the ending are provided, the corresponding boundary coefficient is automatically matched according to the corresponding identification sequence, and the expert group sets a corresponding boundary coefficient matching table for matching; because the number of classified items in the present application is small, the manual setting is most efficient and accurate.
Exemplarily, in a bank application scenario, an account password is secure data, so that a position keyword such as a counter password and an ATM password, which needs to be input, is set, a corresponding search formula is established, a corresponding monitoring video is searched according to the set search formula, and the monitoring video is processed to include only a video segment corresponding to the input password as example data; if necessary, the sample data having the input password is marked as target segment data, the action of inputting the password is taken as feature identification data, and the buffer time is calculated from data such as classification items of the feature identification data. Meanwhile, other application scenarios are provided, for example, where security network protection is required, such as payment codes of a payment place, corresponding monitoring data encryption is required, and therefore the situation that important information of corresponding personnel is leaked due to the fact that the corresponding monitoring data is invaded and leaked is avoided.
The encryption module is used for encrypting the monitoring data, and the specific method comprises the following steps:
acquiring video monitoring data, dividing the acquired video monitoring data into a normal video segment and a segmented video segment, wherein the normal video segment is video data of a non-segmented video segment in the video monitoring data, and acquiring target encrypted data of the normal video segment; performing induction processing on the segmented video segments to obtain an induced video, splicing the induced video with the normal video segments to obtain a video to be encrypted, and encrypting the video to be encrypted through target encryption data to obtain monitoring encrypted video data;
setting unordered encrypted data of the divided video segments, processing the divided video segments through the set unordered encrypted data to obtain encrypted processing data and recorded data, and sending the encrypted processing data to a database for storage; setting an encryption substitution model, setting verification data corresponding to the recording data, and inputting the recording data and the verification data into the encryption substitution model; the encryption replacement model is used for recovering the segmented video segments and replacing the induced video in the video to be encrypted.
The verification data can be in a verification mode of password, fingerprint, face verification and the like, and is set according to actual needs.
When decryption is needed, the monitoring encrypted video data are decrypted through the target encrypted data, and a video to be encrypted is obtained; performing information verification of the encryption replacement model, namely verifying whether the information is in accordance with preset verification data; and when the verification is successful, identifying the corresponding recorded data, acquiring the corresponding encrypted processing data from the database, recovering the encrypted processing data into a segmented video segment by the encryption replacement model, and replacing the induced video in the video to be encrypted.
Based on the above description, the encryption replacement model may be established by using the existing technology, for example, a way of training a neural network model, a way of setting a quantum usb disk, and the like, and in order to further increase the security of encrypted data, the encryption replacement model may also be set to be only capable of running on a specified device, and verification such as IP address needs to be performed.
The method for acquiring the target encrypted data of the normal video segment comprises the following steps:
acquiring current video encryption data comprising data such as an encryption algorithm, an encryption key, a decryption algorithm, an applicable format and the like, setting encryption characteristic data of each video encryption data, and converting the set encryption characteristic data into an encryption characteristic vector; setting a standard requirement template, filling options according to the set standard requirement template by a manager to obtain target encryption requirement data, converting the target encryption requirement data into requirement vectors, calculating matching values between the requirement vectors and the encryption characteristic vectors, and selecting corresponding video encryption data as target encryption data according to the calculated matching values.
The encrypted feature data includes feature data suitable for which video data formats, encryption modes, encryption or decryption efficiencies, and the like, and is specifically set by an expert group according to a requirement target of the application field of the application.
The standard requirement template is set according to application requirement targets in each application range, corresponds to corresponding feature items in the encrypted feature data one by one, can be set manually, and establishes a corresponding target data conversion table, namely, each target requirement item is provided with a value corresponding to a possible target requirement.
And the manager fills in the options according to the set standard requirement template and selects according to the target requirement of each target requirement item.
The method for calculating the matching value between the demand vector and each encrypted feature vector comprises the following steps:
marking the demand vector as XQ ═ (P1, P2, Pi, … …, Pn), wherein i represents the corresponding characteristic item or the target demand item, i ═ 1, 2, … …, n, and n is a positive integer; marking the encrypted feature vector as TZ (H1, H2, Hi, … …, Hn), obtaining a weight coefficient beta i, which refers to the weight coefficient of each feature item or target demand item, setting according to the demand of corresponding managers, and calculating the weight coefficient according to a formula
A match value between the demand vector and the encrypted feature vector is calculated.
The method for setting unordered encrypted data of the divided video segment comprises the following steps:
identifying the duration and the data format of the segmented video segments, and matching a corresponding data segmentation scheme, wherein the data segmentation scheme is used for segmenting the segmented video segments into a plurality of unit segments again, performing unordered combination arrangement, recording a corresponding combination arrangement sequence, acquiring a splicing record of a corresponding induced video, and integrating the combination arrangement sequence and the corresponding splicing record into recorded data.
The inducing processing of the segmented video segments is to change corresponding characteristic actions or display pictures and the like based on the current video processing technology, a corresponding intelligent processing model can be established based on the corresponding processing technology, and the inducing processing of the segmented video segments is performed through the intelligent processing model, and the specific unpublished part is common knowledge in the field.
In another embodiment, the operation method of the encryption module is different from that in the previous embodiment in that:
because the induction processing of the segmentation of the video segments is complicated, for application scenes with low encryption requirements, the video segments can be replaced by setting a standard replacement mode, or the video segments are not directly replaced, other videos are not added, and the normal video segments are spliced by themselves.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the present invention.
Claims (8)
1. The network security monitoring data encryption system is characterized by comprising an identification and segmentation module, an encryption module, a database and a server;
performing key data identification through an identification and segmentation module, and segmenting corresponding monitoring video data to obtain segmented video segments;
the encryption module is used for encrypting the monitoring data, acquiring video monitoring data, dividing the acquired video monitoring data into a normal video segment and a segmentation video segment, and acquiring target encryption data of the normal video segment; performing induction processing on the segmented video segments to obtain an induced video, splicing the induced video with the normal video segments to obtain a video to be encrypted, and encrypting the video to be encrypted through target encryption data to obtain monitoring encrypted video data;
setting unordered encrypted data of the divided video segments, processing the divided video segments through the set unordered encrypted data to obtain encrypted processing data and recorded data, and sending the encrypted processing data to a database for storage; setting an encryption substitution model, setting verification data corresponding to the recording data, and inputting the recording data and the verification data into the encryption substitution model; the encryption replacement model is used for recovering the segmented video segments and replacing the induced video in the video to be encrypted.
2. The system for encrypting network security monitoring data according to claim 1, wherein the method for identifying the partitioning module comprises:
identifying an industry field corresponding to the monitoring data, matching a corresponding keyword set and corresponding example data according to the identified industry field, carrying out encryption segment phrase combination to obtain example data corresponding to an encryption segment phrase, marking the example data as target segment data, setting corresponding feature identification data according to the target segment data, setting corresponding buffer time according to the obtained feature identification data, and establishing an identification segmentation model according to the buffer time and the feature identification data;
and acquiring monitoring video data in real time, and identifying and segmenting the monitoring video data through an identification and segmentation model to obtain corresponding segmented video segments.
3. The system of claim 2, wherein the means for matching the corresponding set of keywords to the corresponding sample data based on the identified industry domain comprises:
setting an application field, acquiring security keywords of the application field, establishing a search formula, performing data search according to the established search formula to acquire field search data, setting corresponding example data according to the acquired field search data, associating the example data with security keyword combinations corresponding to the search formula, integrating the security keywords in the application field into a keyword set, establishing a field matching library, acquiring the industry field needing to be matched, inputting the industry field into the field matching library for matching, and acquiring the corresponding keyword set, security keyword combinations and corresponding example data.
4. The system for encrypting network security monitoring data according to claim 2, wherein the method for setting the corresponding buffering time according to the obtained feature identification data comprises:
and obtaining classification items corresponding to the feature identification data, matching corresponding initial values, marking as CS, setting corresponding buffer correction values according to the feature identification data, marking as HC, matching corresponding division coefficients according to the obtained classification items, marking as alpha, and calculating buffer time according to a time formula.
5. The system of claim 4, wherein the time formula is Tc ═ α × (b1 × CS + b2 × HC), where b1 and b2 are proportional coefficients, and the value ranges from 0< b1 ≦ 1 and 0< b2 ≦ 1.
6. The system for encrypting network security monitoring data according to claim 1, wherein the method for acquiring the target encrypted data of the normal video segment comprises:
acquiring video encryption data, setting encryption characteristic data of each video encryption data, and converting the set encryption characteristic data into encryption characteristic vectors; setting a standard requirement template, filling options according to the set standard requirement template by a manager to obtain target encryption requirement data, converting the target encryption requirement data into requirement vectors, calculating matching values between the requirement vectors and the encryption characteristic vectors, and selecting corresponding video encryption data as target encryption data according to the calculated matching values.
7. The system according to claim 6, wherein the means for calculating the matching value between the requirement vector and each of the encrypted feature vectors comprises:
marking the demand vector as XQ (P1, P2, Pi, … …, Pn), wherein i is 1, 2, … …, n is a positive integer; marking the encrypted feature vector as TZ ═ (H1, H2, Hi, … …, Hn), obtaining the weight coefficient beta i, and obtaining the weight coefficient beta i according to the formula
A match value between the demand vector and the encrypted feature vector is calculated.
8. The system for encrypting network security monitoring data according to claim 1, wherein the method for decrypting the monitoring encrypted video data comprises:
decrypting the monitoring encrypted video data through the target encrypted data to obtain a video to be encrypted; and performing information verification of the encryption replacement model, not performing operation when the verification fails, identifying corresponding recorded data when the verification succeeds, acquiring corresponding encryption processing data from the database, and recovering the encryption processing data into a segmented video segment and replacing the induced video in the video to be encrypted by the encryption replacement model.
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| CN202210645762.1A CN115052171A (en) | 2022-06-08 | 2022-06-08 | Network security monitoring data encryption system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117272281A (en) * | 2023-11-08 | 2023-12-22 | 南京特沃斯清洁设备有限公司 | Visual environment monitoring system based on data Internet of things |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117272281A (en) * | 2023-11-08 | 2023-12-22 | 南京特沃斯清洁设备有限公司 | Visual environment monitoring system based on data Internet of things |
| CN117272281B (en) * | 2023-11-08 | 2024-01-30 | 南京特沃斯清洁设备有限公司 | Visual environment monitoring system based on data Internet of things |
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