CN115600232A - Big data based information safety monitoring system and method - Google Patents
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Abstract
The invention discloses an information safety monitoring system and method based on big data, belonging to the field of information safety monitoring, wherein the information safety monitoring system comprises a data acquisition module, a database, a data simulation module and a mode judgment module, wherein the data acquisition module is used for acquiring dynamic image information of a target user, the database is used for storing the acquired data information and preprocessing the data, the data simulation module is used for performing analog simulation on the acquired data, and the mode judgment module is used for performing computer mode judgment according to an analyzed data result. The gait monitoring system monitors the gait of the target user in real time through the camera device, inputs basic data information, encrypts the whole process, analyzes the knee joint position information of the target user, establishes a knee joint simulation model for analog simulation when the leg is shielded, judges the use mode of the computer according to the analysis result, and improves the monitoring accuracy.
Description
Technical Field
The invention relates to the field of information security monitoring, in particular to an information security monitoring system and method based on big data.
Background
With the advance of science and technology, the rapid development of various computer technologies and network technologies, the development of computers has entered a fast and new era, and computers have been developed from single function and large volume to complex function, small volume, resource networking and the like. The computer is also developed from the original state of only being used for military scientific research to the state of being owned by people, the powerful application function of the computer generates huge market demands, and the performance of the computer is developed towards the direction of miniaturization, networking, intellectualization and giant.
Nowadays, people's daily office is also increasingly unable to keep away from the use of computers, but whether the office computer needs encryption is also called a big problem of people's daily discussion. Not only a large amount of work data but also private marks such as QQ, weChat conversation and the like exist on the office computer, and meanwhile, confidential documents of some companies and even business secrets such as planning schemes, client data, employee compensation data and the like are involved in the office computer, and once the work data is lost and leaked, huge consequences can be caused. However, the office computer is borrowed by others, for example, when the computer of a certain employee suddenly fails, the computer is urgently needed to be used, at this time, if all the office computers set the password, the work of the user will be difficult to be carried out, but the information safety in the computer is difficult to be ensured without setting the password.
Therefore, it is necessary to protect the information security of the computer, monitor the information security of the computer in real time, and facilitate the development of work. Therefore, it is necessary to have a system and a method for monitoring information security based on big data.
Disclosure of Invention
The invention aims to provide an information safety monitoring system and method based on big data, which are characterized in that gait of a target user is monitored in real time through a camera device, basic data information is input, the whole process is encrypted through a skipJack algorithm, knee joint position information of the target user is analyzed, when leg shielding occurs, simulation is carried out on collected data information through establishing a knee joint simulation model, comparison and identification are carried out on the collected data information and data information stored in a database according to an analysis result, and the use mode of a computer is judged so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an information security monitoring system based on big data, the information security monitoring system comprising: the system comprises a data acquisition module, a database, a data simulation module and a mode judgment module;
the data acquisition module is connected with a database, the database is connected with a data simulation module, and the data simulation module is connected with a mode judgment module; the data acquisition module is used for acquiring dynamic image information of a target user, the database is used for storing the acquired data information and preprocessing the data, the data simulation module is used for performing analog simulation on the acquired data, and the mode judgment module is used for performing computer mode judgment according to an analyzed data result.
Furthermore, the data acquisition module comprises a basic information acquisition unit and an image acquisition unit, wherein the basic information acquisition unit is used for acquiring basic information of a computer and a user, such as a computer number, the user and the like, the image acquisition unit is used for acquiring an image of a gait of a target user through the camera equipment and performing image ranging, such as a safety camera and the like, the precision of the camera equipment is not required, professional equipment is not required, and the image acquisition is convenient to realize.
Further, the database comprises a data encryption unit, a data storage unit and a data preprocessing unit, the data encryption unit encrypts data through a skipJack algorithm, the skipJack encryption algorithm is a symmetric block encryption algorithm, the key length is 80 bits, the plaintext and ciphertext lengths are 64 bits, the round number is 32 rounds, the data encryption unit is realized in tamper-resistant hardware, the key length of the skipJack algorithm is 80 bits, and the key amount is 1.2 multiplied by 10 24 24 bits more than 56 bits of DES algorithm, can resist exhaustive key attack, has good statistical properties, and is easy to implement in hardware. The data storage unit is used for storing collected data and analysis data, the data preprocessing unit is used for preprocessing the collected data, and preprocessing and analysis are carried out on shot image data information, so that a data basis is provided for data simulation.
Furthermore, the data simulation module comprises an analog simulation unit and a data comparison unit, the analog simulation unit is used for performing analog simulation on collected data by establishing a knee joint simulation model, and the data comparison unit is used for calling database data for comparison according to a simulation result and a preprocessed analysis result, so that the accuracy of the data is ensured, and the obtained result is more accurate.
Further, the mode judging module is used for judging the use mode of the computer, and according to the analysis result, when the collected target user is the user of the computer, the computer enters a user mode, the user mode is a user daily office system, and the privacy information file of the user is stored in the system; when the collected target user is not the user of the computer, the computer enters a visitor mode, the visitor mode is a basic system only containing daily use software, the contained software comprises a browser, a WeChat and the like, private information of the user of the computer does not exist in the system, leakage of the information of the user when the user uses the computer by other people can be effectively avoided, and information safety of the computer is guaranteed.
An information safety monitoring method based on big data comprises the following steps:
s1, acquiring image data information through a camera device, and inputting basic data information into a database;
s2, under the condition that the target user can be completely shot, preprocessing the acquired data information, encrypting the data in the whole process, and storing the acquired data and the analysis data;
s3, under the condition that the leg of the target user is shielded, performing analog simulation on the acquired data information by establishing a knee joint simulation model, and comparing the data with a database to obtain information data of the target user monitoring the use of a computer;
and S4, judging the use mode of the computer according to the analysis result.
Furthermore, in step S2, image acquisition is performed through a camera device, and gait of a target user is preprocessed and analyzed, gait recognition is a new biological feature recognition technology, and aims to perform identity recognition through the walking posture of people, compared with other biological recognition technologies, gait recognition has the advantages of non-contact remote distance and difficulty in camouflage, the application cost is lower, the requirement on hardware equipment is not high, a camera for acquiring information is general, the recognition distance is longer, and recognition can be performed within 50 meters;
under the condition that the target user can be completely shot, acquiring soft biological characteristics and hard biological characteristics of the target user, wherein the soft biological characteristics are physical or behavior characteristics capable of providing personal information, such as sex, skin color, height, body type and the like; the hard biological characteristics are personal specific information data, such as joint point positions and the like, knee joint position points and hip joint position points of a target user in the walking process are collected in real time through collected images by using an open technology, the open is a human posture identification item, is a bottom-up algorithm, can realize posture estimation of human body actions, facial expressions, finger motions and the like, can detect all joint points of a human body in the images, and then distributes the detected joint points to each corresponding person. The connecting position points form a sine wave curve y which is:
wherein, a is the knee joint amplitude of the target user and is in units of centimeters, l is the step length of the target user and is in units of centimeters, x is the knee joint position point, and k is the offset distance, which reflects the upward movement or the downward movement of the image;
and comparing and identifying the knee joint position point curve of the target user analyzed in real time with the curve stored in the database, analyzing whether the knee joint position point curve is consistent with the stored curve or not, and storing the analysis result into the database.
Further, in step S3, when the target user is in a walking process and has a leg shielding situation, for example, the knee is shielded by a table or a chair or the number of people in the captured image is large, the collected data information is subjected to analog simulation by establishing a knee joint simulation model;
when the target user can be completely shot through the camera equipment, image ranging is carried out, the length of the thigh of the target user is measured to be d, the unit is centimeter, and the target user is shotThe target user is placed in a coordinate system, which can be established by himself, for example, by using the camera as an origin. Measuring the hip joint position coordinate as (x) i ，y i ) The hip joint position coordinate set of the target user is P = { (x) 1 ，y 1 )，(x 2 ，y 2 )，…，(x n ，y n ) N represents the collected n position coordinates, the image shows that the included angle between the thigh and the vertical direction is theta i at the moment i in the walking process of the target user, and the set of the included angles is R = { theta = theta 1 ，θ 2 ，…，θ n }；
Let the knee joint position coordinate be (X) i ，Y i ) The set of knee joint position coordinates of the target user is Q = { (X) 1 ，Y 1 )，(X 2 ，Y 2 )，…，(X n ，Y n ) Determine knee joint position coordinates by the following formula:
determining the knee joint position through a knee joint simulation model, and connecting to form a sine wave curve Y according to knee joint position points as follows:
wherein, A is the knee joint amplitude of the target user in centimeter, L is the distance of the target user in one step in centimeter, X is the knee joint position point, and K is the offset distance.
And comparing and identifying the knee joint position point simulation curve of the target user analyzed in real time with the curve stored in the database, analyzing whether the knee joint position point curve is consistent with the stored curve or not, and storing the analysis result into the database.
Further, in step S4, comparing and recognizing the data information stored in the database according to the analysis result, and determining the usage mode of the computer; when the knee joint position point curve of the target user is consistent with the curve stored in the database, the acquired target user is the user of the computer, and the computer enters a user mode; when the curve of the knee joint position point of the target user is inconsistent with the curve stored in the database, the acquired target user is not the user of the computer, and the computer enters a visitor mode.
Compared with the prior art, the invention has the following beneficial effects:
the gait of the target user is monitored in real time through the camera device, basic data information is input into the database, the whole process is encrypted through the skipJack algorithm, and the data security is guaranteed. The knee joint position points of the target user are connected by analyzing the knee joint position information of the target user to form a sine wave curve, and the sine wave curve is compared and judged with the data information stored in the database. When the leg is shielded, the collected data information is subjected to analog simulation by establishing a knee joint simulation model, the position of the knee joint is determined, the position points of the knee joint are connected to form a sine waveform curve, and the sine waveform curve is compared and identified with the data information stored in the database according to an analysis result, so that the use mode of the computer is judged, and the accuracy of data analysis is improved. When the target user is the user person of the computer, the computer enters a user mode, and when the target user is not the user person, the computer enters a visitor mode, so that the data security of the computer is ensured, and meanwhile, when other people borrow the computer, the work of other people is facilitated to be performed, and the use efficiency of the computer is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of the module composition of a big data-based information security monitoring system according to the present invention;
FIG. 2 is a schematic flow chart of a big data-based information security monitoring method according to the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
Referring to fig. 1-2, the present invention provides a technical solution: an information security monitoring system based on big data, the information security monitoring system comprising: the system comprises a data acquisition module, a database, a data simulation module and a mode judgment module;
the data acquisition module is connected with a database, the database is connected with a data simulation module, and the data simulation module is connected with a mode judgment module;
the data acquisition module is used for acquiring dynamic image information of a target user. The data acquisition module comprises a basic information acquisition unit and an image acquisition unit, wherein the basic information acquisition unit is used for acquiring basic information of a computer and a user, such as a computer number, the user and the like, the image acquisition unit is used for acquiring an image of a gait of a target user through the camera equipment and measuring an image distance, such as a safety camera and the like, the precision of the camera equipment is not required, professional equipment is not needed, and the image acquisition is convenient to realize.
The database is used for storing the acquired data information and carrying out data preprocessing. The database comprises a data encryption unit, a data storage unit and a data preprocessing unit, wherein the data encryption unit encrypts data through a skipJack algorithm, the skipJack encryption algorithm is a symmetric block encryption algorithm, the key length is 80 bits, the plaintext and ciphertext lengths are 64 bits, the round number is 32 rounds, the data encryption unit is realized in tamper-resistant hardware, the key length of the skipJack algorithm is 80 bits, and the key amount is 1.2 multiplied by 10 24 24 bits more than 56 bits of DES algorithm, can resist exhaustive key attack, has good statistical properties, and is easy to implement in hardware. The data storage unit is used for storing the collected dataAnd the data preprocessing unit is used for preprocessing the acquired data and preprocessing and analyzing the shot image data information so as to provide a data base for data simulation.
The data simulation module is used for carrying out analog simulation on the acquired data. The data simulation module comprises an analog simulation unit and a data comparison unit, the analog simulation unit carries out analog simulation on collected data by establishing a knee joint simulation model, and the data comparison unit is used for calling and comparing database data according to a simulation result and a preprocessed analysis result, so that the accuracy of the data is ensured, and the obtained result is more accurate.
The mode judging module is used for judging the mode of the computer according to the analyzed data result. According to the analysis result, when the collected target user is the user of the computer, the computer enters a user mode, the user mode is a user daily office system, and the privacy information file of the user is stored in the system; when the collected target user is not the user of the computer, the computer enters a visitor mode, the visitor mode is a basic system only containing daily use software, the contained software comprises a browser, a WeChat and the like, private information of the user of the computer does not exist in the system, leakage of the information of the user when the user uses the computer by other people can be effectively avoided, and information safety of the computer is guaranteed.
An information safety monitoring method based on big data comprises the following steps:
s1, acquiring image data information through a camera device, and inputting basic data information into a database;
s2, under the condition that the target user can be completely shot, preprocessing the acquired data information, encrypting the data in the whole process, and storing the acquired data and the analysis data;
in step S2, image acquisition is carried out through a camera device, the gait of a target user is preprocessed and analyzed, gait recognition is a novel biological feature recognition technology, and aims to carry out identity recognition through the walking posture of people, compared with other biological recognition technologies, the gait recognition has the advantages of non-contact remote distance and difficulty in disguising, the application cost is lower, the requirement on hardware equipment is not high, the camera for acquiring information is used generally, the recognition distance is longer, and the recognition can be carried out within 50 meters;
under the condition that the target user can be completely shot, acquiring soft biological characteristics and hard biological characteristics of the target user, wherein the soft biological characteristics are physical or behavior characteristics capable of providing personal information, such as sex, skin color, height, body type and the like; the hard biological characteristics are personal specific information data, such as joint point positions and the like, knee joint position points and hip joint position points of a target user in the walking process are collected in real time through collected images by using an openposition technology, openposition is a human body posture identification item, is a bottom-up algorithm, can realize posture estimation of human body actions, facial expressions, finger motions and the like, can detect all joint points of a human body in the images, and then distributes the detected joint points to corresponding people. The connecting position points form a sine wave curve y which is:
wherein, a is the knee joint amplitude of the target user and is in centimeter, 1 is the step length of the target user and is in centimeter, x is the knee joint position point, and k is the offset distance, which reflects the upward movement or the downward movement of the image;
and comparing and identifying the knee joint position point curve of the target user analyzed in real time with the curve stored in the database, analyzing whether the knee joint position point curve is consistent with the stored curve or not, and storing the analysis result into the database.
S3, under the condition that the leg of the target user is shielded, performing analog simulation on the acquired data information by establishing a knee joint simulation model, and comparing the data with a database to obtain information data of the target user monitoring the use of a computer;
in step S3, when the target user is in a walking process and has a leg shielding situation, for example, the knee is shielded by a table or a chair or the number of people in the captured image is large, the collected data information is simulated by establishing a knee joint simulation model;
when the target user can be completely shot by the camera equipment, image ranging is carried out, the length of the thigh of the target user is measured to be d, the unit is centimeter, the target user is placed in a coordinate system, and the coordinate system can be automatically established, for example, the coordinate system is established by taking the camera equipment as the origin. Measuring the hip joint position coordinate as (x) i ，y i ) The hip joint position coordinate set of the target user is P = { (x) 1 ，y 1 )，(x 2 ，y 2 )，…，(x n ，y n ) N represents the collected n position coordinates, and the image shows that the included angle between the thigh and the vertical direction is theta at the moment i in the walking process of the target user i The set of angles is R = { theta = 1 ，θ 2 ，…，θ n }；
Let the knee joint position coordinate be (X) i ，Y i ) The set of knee joint position coordinates of the target user is Q = { (X) 1 ，Y 1 )，(X 2 ，Y 2 )，…，(X n ，Y n ) Determine knee position coordinates by the following equation:
determining the position of the knee joint through a knee joint simulation model, and connecting to form a sine waveform curve Y according to the knee joint position points as follows:
wherein, A is the knee joint amplitude of the target user in centimeter, L is the distance of the target user in one step in centimeter, X is the knee joint position point, and K is the offset distance.
And comparing and identifying the knee joint position point simulation curve of the target user analyzed in real time with the curve stored in the database, analyzing whether the knee joint position point curve is consistent with the stored curve or not, and storing the analysis result into the database.
And S4, judging the use mode of the computer according to the analysis result.
In step S4, comparing and identifying the data information stored in the database according to the analysis result, and judging the use mode of the computer; when the curve of the knee joint position point of the target user is consistent with the curve stored in the database, the acquired target user is the user of the computer, and the computer enters a user mode; when the curve of the knee joint position point of the target user is inconsistent with the curve stored in the database, the acquired target user is not the user of the computer, and the computer enters a visitor mode.
The first embodiment is as follows:
under the condition that a target user can be completely shot, the amplitude of the knee joint of the target user is 5 cm, the step length is 45 cm, the offset distance is 5, the position of the knee joint is collected in real time through a collected image, and a sine wave curve is formed by connecting position points
If the sine wave curve of knee joint of the computer user in the database is consistent with one, entering into user mode, if so, entering into user mode
Judging whether the target user is the user of the computer by comparison, and entering a visitor mode;
when the target user walks and has leg shielding conditions, such as the number of people in the images collected by the capturing or the knee being shielded by tables and chairs, the simulation of the collected data information is carried out by establishing a knee joint simulation model;
when the target user can be completely shot by the camera equipment, image ranging is carried out, the thigh length of the target user is measured to be 40 cm, the target user is placed in a coordinate system, and the hip joint position at the moment i is measuredThe coordinate is (60, 60), and the included angle theta between the thigh and the vertical direction i ＝30°，
Let the knee joint position coordinate be (X) i ，Y i ) Determining knee joint position coordinates by the following formula:
finding X i ＝80，
Determining the knee joint position coordinate at the moment i
In a similar way, the knee joint position of the user is continuously collected, and the knee joint position points are connected to form a sine wave curve;
determining the position of the knee joint through a knee joint simulation model, and forming a sine waveform curve Y =accordingto the connection of knee joint position points
If the knee joint curve of the computer user in the database is consistent with the knee joint curve, the user is judged to be the user himself, the system enters a user mode, and if the knee joint curve of the computer user is not consistent with the knee joint curve, the system enters a visitor mode;
it is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The utility model provides an information security monitoring system based on big data which characterized in that: the information safety monitoring system comprises: the system comprises a data acquisition module, a database, a data simulation module and a mode judgment module;
the data acquisition module is connected with a database, the database is connected with a data simulation module, and the data simulation module is connected with a mode judgment module; the data acquisition module is used for acquiring dynamic image information of a target user, the database is used for storing the acquired data information and preprocessing the data, the data simulation module is used for performing analog simulation on the acquired data, and the mode judgment module is used for performing computer mode judgment according to an analyzed data result.
2. The big data-based information security monitoring system according to claim 1, wherein: the data acquisition module comprises a basic information acquisition unit and an image acquisition unit, wherein the basic information acquisition unit is used for acquiring basic information of a computer and a user, and the image acquisition unit is used for acquiring an image of a gait of a target user through camera equipment and performing image ranging.
3. The big data-based information security monitoring system according to claim 2, wherein: the database comprises a data encryption unit, a data storage unit and a data preprocessing unit, wherein the data encryption unit encrypts data through a skipJack algorithm, the data storage unit is used for storing collected data and analyzed data, and the data preprocessing unit is used for preprocessing the collected data and preprocessing and analyzing shot image data information.
4. The big data-based information security monitoring system according to claim 3, wherein: the data simulation module comprises an analog simulation unit and a data comparison unit, the analog simulation unit carries out analog simulation on collected data by establishing a knee joint simulation model, and the data comparison unit is used for calling and comparing database data according to a simulation result and a preprocessed analysis result.
5. The big data-based information security monitoring system according to claim 4, wherein: the mode judging module is used for judging the use mode of the computer, and when the acquired target user is the user of the computer, the computer enters a user mode according to the analysis result; when the collected target user is not the user of the computer, the computer enters a guest mode.
6. A big data-based information security monitoring method is characterized in that: the method comprises the following steps:
s1, acquiring image data information through a camera device, and inputting basic data information into a database;
s2, under the condition that the target user can be completely shot, preprocessing the acquired data information, encrypting the data in the whole process, and storing the acquired data and the analysis data;
s3, under the condition that the leg of the target user is shielded, performing analog simulation on the acquired data information by establishing a knee joint simulation model, and comparing the data with a database to obtain information data of the target user monitoring the use of a computer;
and S4, judging the use mode of the computer according to the analysis result.
7. The big data-based information security monitoring method according to claim 6, wherein: in the step S2, image acquisition is carried out through camera equipment, and preprocessing analysis is carried out on the gait of the target user;
under the condition that can shoot the target user completely, gather target user's soft biological characteristic and hard biological characteristic, through the image of gathering, utilize openposition technique, gather target user's knee joint position point at the walking in-process in real time, the hookup location point forms sinusoidal wave curve y and is:
wherein a is the knee joint amplitude of the target user, l is the distance of one step of the target user, x is the knee joint position point, and k is the offset distance;
and comparing and identifying the knee joint position point curve of the target user analyzed in real time with the curve stored in the database, analyzing whether the knee joint position point curve is consistent with the stored curve or not, and storing the analysis result into the database.
8. The big data-based information security monitoring method according to claim 7, wherein: in the step S3, when the leg shielding condition occurs in the walking process of the target user, the collected data information is subjected to analog simulation by establishing a knee joint simulation model;
when the target user can be completely shot by the camera equipment, image ranging is carried out, the thigh length of the target user is measured to be d, the unit is centimeter, the target user is placed in a coordinate system, and the hip joint position coordinate is measured to be (x) i ，y i ) The hip joint position coordinate set of the target user is P = { (x) 1 ，y 1 )，(x 2 ，y 2 )，…，(x n ，y n ) N represents the collected n position coordinates, and the image shows that the included angle between the thigh and the vertical direction is theta at the moment i in the walking process of the target user i Angle of inclinationSet as R = { theta = 1 ，θ 2 ，…，θ n }；
Let the knee joint position coordinate be (X) i ，Y i ) The set of knee joint position coordinates of the target user is Q = { (X) 1 ，Y 1 )，(X 2 ，Y 2 )，…，(X n ，Y n ) Determine knee joint position coordinates by the following formula:
determining the position of the knee joint through a knee joint simulation model, and connecting to form a sine waveform curve Y according to the knee joint position points as follows:
wherein, A is the knee joint amplitude of the target user, L is the distance of one step of the target user, X is the knee joint position point, and K is the offset distance;
and comparing and identifying the knee joint position point simulation curve of the target user analyzed in real time with the curve stored in the database, analyzing whether the knee joint position point curve is consistent with the stored curve or not, and storing the analysis result into the database.
9. The big data-based information security monitoring method according to claim 8, wherein: in step S4, comparing and identifying the data information stored in the database according to the analysis result, and judging the use mode of the computer; when the curve of the knee joint position point of the target user is consistent with the curve stored in the database, the acquired target user is the user of the computer, and the computer enters a user mode; when the curve of the knee joint position point of the target user is inconsistent with the curve stored in the database, the acquired target user is not the user of the computer, and the computer enters a visitor mode.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116027903A (en) * | 2023-01-30 | 2023-04-28 | 大庆市壹零零壹数据服务有限公司 | Computer network security analysis system and method based on big data |
| CN116467368A (en) * | 2023-06-13 | 2023-07-21 | 北京大众在线网络技术有限公司 | Safety monitoring method and system based on big data analysis |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116027903A (en) * | 2023-01-30 | 2023-04-28 | 大庆市壹零零壹数据服务有限公司 | Computer network security analysis system and method based on big data |
| CN116027903B (en) * | 2023-01-30 | 2023-09-29 | 中软国际科技服务有限公司 | Computer network security analysis system and method based on big data |
| CN116467368A (en) * | 2023-06-13 | 2023-07-21 | 北京大众在线网络技术有限公司 | Safety monitoring method and system based on big data analysis |
| CN116467368B (en) * | 2023-06-13 | 2023-10-24 | 北京大众在线网络技术有限公司 | Safety monitoring method and system based on big data analysis |
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