Data security management and control method based on data application management platform

Abstract

The present invention discloses a data security control method based on a data application management platform, which relates to the field of data security technology and includes the following steps: obtaining first data of the location of a target device, and inputting a preset device response time prediction model to generate a device response time prediction value; establishing a connection between the target device and the data application management platform to obtain a device response time measured value; obtaining second data of the target device, performing data analysis, and determining an allowable range of device response time error; combining the device response time prediction value, the device response time measured value, and the device response time error allowable range with a preset device risk judgment formula to perform data analysis, determine whether the device has a risk, and process the risky device. The present application is used to solve the problem of data risk caused by the connection between a data platform and a lower computer device.

Description

Technical Field

[0001] The present invention relates to the field of data security technology, and more specifically, to a data security management and control method based on a data application management platform. Background Art

[0002] Data security management methods refer to a series of policies, processes and technical means adopted to ensure the confidentiality, integrity and availability of data. These methods are designed to prevent data leakage, loss, tampering or unauthorized access. As the scale and complexity of data increase, enterprises and organizations pay more and more attention to data security management to ensure the effective use and sharing of data under the premise of compliance and security.

[0003] For example, a data security management method and system is announced in the invention patent with announcement number: CN113962808B, which discloses a data security management method and system, belonging to the field of data security technology, including: storing the timestamp of operating the first data, the content of the first data after the operation, the hash value of the content of the first data after the operation, the hierarchical identification of the first data after the operation and the first account number at the first data storage location before the operation; encrypting the first data before and after the operation to form the first encrypted data before and after the operation; forming the first data transaction together with the first encrypted data before and after the operation and the timestamp, the first account number and the hierarchical identification of the first data when operating the first data; and being able to systematically and effectively record the operations of the entire process from data generation to destruction, and the recorded content is verifiable, so as to facilitate the tracing of the flow of the entire life cycle of data.

[0004] For example, the invention patent with announcement number CN112711757B announces a data security centralized control method and system based on a big data platform, which provides a data security centralized control method and system based on a big data platform, and the method includes: based on the big data platform, obtaining the original data of different target units, and fusion storage and classification storage of the original data of the association relationship, and constructing a first identification model based on the fusion storage result and a second identification model set based on the classification storage result; constructing a third identification model based on the legal source information, use compliance information, security transmission information, and security audit information of the original data of the association relationship; collecting business data, and preprocessing the business data, respectively identifying the preprocessing results based on the identification model, and screening and calibrating abnormal data and security data according to the second identification result, and controlling the security data in accordance with the standard control method, and controlling the abnormal data in accordance with the centralized control method. Through the corresponding method, effective control is achieved, and the security protection of data is improved.

[0005] The above disclosed technical solutions have at least the following technical problems:

[0006] Existing technologies achieve the purpose of enhancing data security by storing, encrypting and managing data on the data platform side, but they cannot eliminate the risks brought by establishing connections with the lower-level device side. There is a possibility that the data platform will be leaked due to attacks from the lower-level device side.

[0007] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0008] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a data security management and control method based on a data application management platform. By determining the allowable range of device response time error, it is judged whether the device is at risk and processed according to the predicted value and measured value of the device response time, so as to solve the problem of data risks caused by the connection between the data platform and the lower-level device.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] The data security management and control method based on the data application management platform includes the following steps: obtaining first data of the location of the target device, and inputting a preset device response time prediction model to generate a device response time prediction value; establishing a test connection between the target device and the data application management platform to obtain the device response time actual value; obtaining second data of the target device, performing data analysis, and determining the allowable range of device response time error; combining the device response time prediction value, the device response time actual value, and the device response time error allowable range with a preset device risk judgment formula for data analysis to determine whether the device is at risk, and processing the risky device.

[0011] In a preferred embodiment, the preset device response time prediction model is specifically constructed by: obtaining the first data of all connected devices, the first data including the signal interference strength at the device location and the weather data of the data transmission path; establishing a test connection between all devices and the data application management platform to obtain the historical response time of all connected devices; preprocessing and feature extraction of the obtained first data to construct a first historical data set as a training set for the model; with the goal of minimizing the gap between the device response time prediction value and the device historical response time, constructing a device response prediction model based on a neural network algorithm and training it, and outputting the device response time prediction value.

[0012] In a preferred embodiment, the second data of the target device is obtained, and data analysis is performed to determine the allowable range of device response time error, specifically: the second data includes device security protection strength information and the number of historical dangerous behaviors of the user; the target device is divided into corresponding device security protection levels according to a preset division standard combined with the device security protection strength information; the allowable relative error of the device response time is obtained according to the device security protection level of the target device and the number of historical dangerous behaviors of the user, combined with a preset error limiting formula; the allowable range of the device response time error is determined according to the allowable relative error of the device response time.

[0013] In a preferred embodiment, the device security protection strength information includes physical layer security protection information, software layer security protection information and network layer security protection information.

[0014] In a preferred embodiment, the predicted value of the device response time, the measured value of the device response time and the allowable range of the device response time error are combined with a preset device risk judgment formula to perform data analysis to determine whether the device is risky and process the risky device, specifically: obtaining the predicted value of the target device response time in real time, performing several test connections to obtain the measured value of the device response time of the target device, and obtaining the allowable range of the device response time error; obtaining the first mean of the error between the predicted value and the measured value of the target device response time for several test connections according to a preset error mean calculation formula; if the first mean is within the allowable range of the device response time error, the target device is not processed; if the first mean is outside the allowable range of the device response time error, the target device is marked as a risky device and the risky device is processed.

[0015] The technical effects and advantages of the data security management and control method based on the data application management platform of the present invention are as follows:

[0016] The present invention determines the permissible range of device response time error, determines whether the device is at risk and processes it according to the predicted value and measured value of the device response time, and effectively solves the risk problem caused by establishing a connection between the data application management platform and the lower computer device. By testing the device response time and comparing it with the predicted device response time, it helps to identify the possible hijacking risk of the device, thereby reducing the possibility of data leakage caused by attacks on the data application management platform. By adjusting the permissible range of device response time error according to the device security protection level and the number of historical dangerous behaviors of the user, the accuracy of device risk judgment is changed, making the device risk judgment more accurate, reducing the possibility of misjudgment, and reducing the probability of data security risks while also avoiding overreaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1A flow chart of a data security management and control method based on a data application management platform provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1, Figure 1 A data security management and control method based on a data application management platform is given, which includes the following steps:

[0020] S1, obtaining first data of the location of the target device, and inputting a preset device response time prediction model to generate a device response time prediction value;

[0021] S2, establish a test connection between the target device and the data application management platform to obtain the actual measured value of the device response time;

[0022] S3, obtaining second data of the target device, performing data analysis, and determining an allowable range of device response time error;

[0023] S4, combining the predicted value of the device response time, the measured value of the device response time and the allowable range of the device response time error with a preset device risk judgment formula to perform data analysis, determine whether the device has risks, and handle the risky devices.

[0024] In this embodiment, the preset device response time prediction model is specifically constructed as follows:

[0025] Acquire first data of the connected device, the first data including signal interference intensity at a location of the device and weather data of a data transmission path;

[0026] Establish a test connection between the device and the data application management platform to obtain the historical response time of the connected device;

[0027] Preprocessing and feature extraction are performed on the acquired first data to construct a first historical data set as a training set for the model;

[0028] With the goal of minimizing the gap between the predicted value of device response time and the historical response time of the device, a device response prediction model is built and trained based on the neural network algorithm to output the predicted value of device response time.

[0029] In this embodiment, the data transmission path weather data includes the temperature and humidity of the data transmission path.

[0030] In this embodiment, the formula for the predicted value of the device response time is as follows:

[0031]

[0032] In the formula, is the predicted value of device response time, is the input feature, and is the weight matrix, is the activation function, and is the bias term.

[0033] It should be noted that the test connection refers to a single-round data transmission interaction test connection performed according to a network protocol, which is different from a normal connection.

[0034] In this embodiment, the second data of the target device is obtained, and the data is analyzed to determine the allowable range of the device response time error, specifically:

[0035] The second data includes device security protection strength information and the number of historical dangerous behaviors of users;

[0036] According to the preset classification standards and the equipment security protection strength information, the target equipment is classified into corresponding equipment security protection levels;

[0037] According to the device security protection level of the target device and the number of historical dangerous behaviors of the user, combined with the preset error limit formula, the allowable relative error of the device response time is obtained;

[0038] The permissible range of device response time error is determined based on the permissible relative error of the device response time.

[0039] In this embodiment, the device security protection strength information includes physical layer security protection information, software layer security protection information and network layer security protection information.

[0040] In this embodiment, Table 1 shows a device data security protection level classification standard of the data security control method based on the data application management platform of the present invention. Among them, physical level security protection refers to the protection to ensure that the device itself is not physically damaged or unauthorized access; software level security protection refers to the protection to ensure that the data is not damaged or stolen by malware, viruses, Trojans when the device is running; network level security protection refers to the protection measures taken by the device when it transmits data in the network, facing the threats of network attacks, data leakage and man-in-the-middle attacks.

[0041] Table 1

[0042]

[0043] In this embodiment, the preset error limiting formula is specifically as follows:

[0044]

[0045] In the formula, is the allowable relative error of the device response time, is the preset constant, is the weight coefficient of the impact of the equipment security protection level on the equipment response time, is the weight coefficient of the impact of the number of historical dangerous behaviors of the user on the device response time, is the equipment safety protection level, The number of historical risky behaviors of the user.

[0046] It should be noted that the preset constant It is an empirical constant for adjusting the error according to actual conditions.

[0047] In this embodiment, the allowable range of the device response time error is:

[0048]

[0049] In the formula, is the predicted value of device response time, is the allowable relative error of the device response time, is the symbol for a closed interval.

[0050] It should be noted that the positive correlation between the device data security protection strength and the allowable error of the device response time is mainly reflected in: if the device data security protection strength is high, the code element reception and transmission must strictly comply with the transmission protocol, making the error correction conditions more stringent, thereby affecting the device response time, and the allowable error is correspondingly expanded; if the device data security protection strength is low, the risk of the device being hijacked increases, and the requirements for the device response time are more stringent, so the allowable error is correspondingly reduced.

[0051] Furthermore, historical user risky behaviors refer to historical attacks or sabotage behaviors initiated by users against the data application management platform, as well as historical attacks or sabotage behaviors initiated by devices bound to users. The negative correlation between the number of historical user risky behaviors and the allowable error of device response time is mainly reflected in the following aspects: if the number of historical user risky behaviors is large, it means that the user and the device bound to it are likely to cause data security risks, so the allowable error of the device response time used for risk judgment is correspondingly reduced.

[0052] In this embodiment, the predicted value of the device response time, the measured value of the device response time, and the allowable range of the device response time error are combined with a preset device risk judgment formula to perform data analysis to determine whether the device has risks, and process the risky devices, specifically:

[0053] Obtain the predicted value of the target device response time in real time, perform several test connections to obtain the measured value of the target device response time, and obtain the allowable range of the device response time error;

[0054] According to a preset error mean calculation formula, a first mean value of the error between the predicted value and the measured value of the response time of the target device connected for several tests is obtained;

[0055] If the first mean value is within the permissible range of the device response time error, the target device is not processed;

[0056] If the first mean value is outside the permissible range of the device response time error, the target device is marked as a risky device and the risky device is processed.

[0057] The preset error mean calculation formula is as follows:

[0058]

[0059] In the formula, is the first mean value of the error between the predicted value and the measured value of the target device response time for several test connections, The number of test connections. For the The predicted value of the device response time for the first test connection, For the The actual measured value of the device response time for the first test connection.

[0060] The preset equipment risk judgment formula is specifically as follows:

[0061]

[0062] In the formula, is the equipment risk judgment value, The function is to take , The larger of the two values, is the predicted value of device response time, is the allowable relative error of the device response time, It is the first mean value of the error between the predicted value and the measured value of the target device response time for several test connections.

[0063] It should be noted that if the device risk judgment value is 0, it means that the mean error is within the allowable range of the device response time error; if the device risk judgment value is not 0, it means that the mean error is outside the allowable range of the device response time error.

[0064] This embodiment determines the permissible range of device response time error, determines whether the device is at risk based on the predicted value and measured value of the device response time, and performs processing, thereby effectively solving the risk problem caused by establishing a connection between the data application management platform and the lower-level device. By testing the device response time and comparing it with the predicted device response time, it helps to identify the possible risk of hijacking the device, thereby reducing the possibility of data leakage caused by attacks on the data application management platform. By adjusting the permissible range of device response time error based on the device security protection level and the number of historical dangerous behaviors of the user, the accuracy of device risk judgment is changed, making the device risk judgment more accurate, reducing the possibility of misjudgment, and reducing the probability of data security risks while also avoiding overreaction.

[0065] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0066] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0067] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0068] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0069] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0070] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A data security management and control method based on a data application management platform, characterized in that: The following steps are involved: Acquire first data of the location of the target device, and input a preset device response time prediction model to generate a device response time prediction value, wherein the first data includes signal interference intensity at the location of the device and weather data of a data transmission path; Establish a connection between the target device and the data application management platform to obtain the actual measured value of the device response time; Acquire the second data of the target device, divide the target device into corresponding device security protection levels according to the preset division standard combined with the device security protection strength information, and obtain the allowable relative error of the device response time in combination with the preset error limiting formula according to the device security protection level of the target device and the number of historical dangerous behaviors of the user, the positive correlation between the device security protection strength and the allowable error of the device response time, the negative correlation between the number of historical dangerous behaviors of the user and the allowable error of the device response time, and the allowable relative error of the device response time according to the allowable relative error of the device response time. Determine the allowable range of the device response time error, and the second data includes the device security protection strength information and the number of historical dangerous behaviors of the user; The predicted value of the equipment response time, the measured value of the equipment response time and the allowable range of the equipment response time error are combined with the preset equipment risk judgment formula for data analysis to determine whether the equipment has risks and handle the risky equipment.

2. The data security management and control method based on the data application management platform according to claim 1 is characterized in that: The specific construction method of the preset device response time prediction model is as follows: Acquire first data of the connected device; Establish a test connection between the device and the data application management platform to obtain the historical response time of the connected device; Preprocessing and feature extraction are performed on the acquired first data to construct a first historical data set as a training set for the model; With the goal of minimizing the gap between the predicted value of device response time and the historical response time of the device, a device response prediction model is built and trained based on the neural network algorithm to output the predicted value of device response time.

3. The data security management and control method based on the data application management platform according to claim 2 is characterized in that: The device security protection strength information includes physical layer security protection information, software layer security protection information and network layer security protection information.

4. The data security management and control method based on the data application management platform according to claim 3 is characterized in that: The predicted value of the device response time, the measured value of the device response time and the allowable range of the device response time error are combined with a preset device risk judgment formula to perform data analysis to determine whether the device has risks and process the risky devices, specifically: Obtain the predicted value of the target device response time in real time, perform several test connections to obtain the measured value of the target device response time, and obtain the allowable range of the device response time error; According to a preset error mean calculation formula, a first mean value of the error between the predicted value and the measured value of the response time of the target device connected for several tests is obtained; If the first mean value is within the permissible range of the device response time error, the target device is not processed; If the first mean value is outside the permissible range of the device response time error, the target device is marked as a risky device and the risky device is processed.

5. The data security management and control method based on the data application management platform according to claim 4 is characterized in that: The preset error limiting formula is specifically as follows: In the formula, is the allowable relative error of the device response time, is the preset constant, is the weight coefficient of the impact of the equipment security protection level on the equipment response time, is the weight coefficient of the impact of the number of historical dangerous behaviors of the user on the device response time, is the equipment safety protection level, The number of historical risky behaviors of the user.

6. The data security management and control method based on the data application management platform according to claim 5 is characterized in that: The permissible range of the device response time error is: In the formula, is the predicted value of device response time, is the allowable relative error of the device response time, is the symbol for a closed interval.

7. The data security management and control method based on the data application management platform according to claim 6 is characterized in that: The preset error mean calculation formula is as follows: In the formula, is the first mean value of the error between the predicted value and the measured value of the target device response time for several test connections, The number of test connections. For the The predicted value of the device response time for the first test connection, For the The actual measured value of the device response time for the first test connection.

8. The data security management and control method based on the data application management platform according to claim 7 is characterized in that: The preset equipment risk judgment formula is specifically as follows: In the formula, is the equipment risk judgment value, The function is to take , The larger of the two values, is the predicted value of device response time, is the allowable relative error of the device response time, It is the first mean value of the error between the predicted value and the measured value of the target device response time for several test connections.

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