Cloud-based EMM encryption method and system

Abstract

The invention relates to the technical field of information security, in particular to a cloud-based EMM encryption method and system, and the method comprises the following steps: collecting user login data and equipment use data based on mobile equipment operation information, recognizing the time and position information of user login and equipment use, calculating the risk scores of various behavior modes, and storing the risk scores of various behavior modes. And obtaining user behavior identification data. According to the method, the comprehensiveness and the dynamism of data security management are improved by collecting and analyzing the user login data and the equipment use data in real time, identifying unauthorized access and performing risk scoring on the mobile equipment, and encryption parameters are optimized by combining real-time monitoring on the user behavior pattern and the equipment performance and utilizing data sensitivity scoring; the data encryption efficiency and security are improved, the risk of data leakage is reduced by adjusting the updating frequency and parameters of the encryption key, the response speed to security threats is improved, the flexibility of enterprise data protection is enhanced, and the service continuity is maintained.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to a cloud-based EMM encryption method and system. Background Art

[0002] The field of information security technology focuses on protecting data and information systems from unauthorized access, use, disclosure, destruction, and modification, including network security, application security, terminal security, data encryption, identity and access management. It aims to help enterprises and individuals protect financial and privacy data, enhance the confidentiality, integrity and availability of data, support the secure operation of global e-commerce and communications, and combine cloud security, Internet of Things security, and artificial intelligence to protect critical business information and customer data, prevent malicious attacks and data leaks, and ensure business continuity and compliance with relevant regulations.

[0003] Among them, the EMM encryption method is a data protection technology used in enterprise mobile device management. It uses encryption algorithms to protect data stored on and transmitted through mobile devices, prevents data from being accessed by unauthorized third parties when lost or stolen, and protects sensitive information by enhancing the data security of mobile devices, including corporate emails, documents, and business transaction data. By using EMM encryption, it helps enterprises effectively manage and control sensitive data on devices, reduce the risk of data leakage, and meet industry compliance requirements, ensuring the overall security of the enterprise mobile ecosystem.

[0004] Traditional EMM encryption technology relies on static encryption methods and key management, which makes it difficult to effectively adapt to changes in network conditions and the real-time status of device performance. When faced with complex modern network environments and persistent threats, it does not respond in a timely manner and its security measures do not match, leading to security vulnerabilities. When dealing with batches of dispersed mobile devices, it cannot effectively integrate and manage the security policies of the devices, resulting in high management costs and difficulty in ensuring that all devices are in the optimal security state. As a result, when companies face data leaks and multiple network security threats, they have insufficient protection capabilities, leading to financial losses and reputation damage. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a cloud-based EMM encryption method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a cloud-based EMM encryption method, comprising the following steps:

[0007] S1: Based on the mobile device operation information, collect user login data and device usage data, identify the time and location information of user login and device usage, calculate the risk scores of multiple behavior patterns, and obtain user behavior identification data;

[0008] S2: Based on the user behavior identification data, identifying unauthorized access attempts by analyzing abnormal login data, and generating abnormal access identification information;

[0009] S3: Based on the abnormal access identification information, evaluate the real-time network conditions and device performance of the mobile device, calculate the sensitivity scores of various data, and obtain the data risk level score;

[0010] S4: Based on the data risk level score, the performance data of the encryption parameter configuration is analyzed through real-time monitoring and efficiency evaluation, the encryption parameters are adjusted to optimize the efficiency and security of data encryption, and encryption parameter adjustment information is generated;

[0011] S5: Based on the encryption parameter adjustment information, perform a security scan on the network environment and application behavior of the mobile device, evaluate the security status of the network and interactive applications connected to the device, and obtain device risk score data;

[0012] S6: Based on the device risk score data, combined with the risk score of the user behavior pattern and the sensitivity level of the data, the update frequency and encryption parameters of the target mobile device key are adjusted to obtain the mobile device encryption parameters.

[0013] As a further solution of the present invention, the user behavior identification data includes login frequency information, login location information, and device type identification information; the abnormal access identification information includes unauthorized login time points, abnormal login source records, and abnormal login device information; the data risk level score includes the frequency of sensitive file access, data category sensitivity information, and the degree of matching between access behavior and risk pattern; the encryption parameter adjustment information includes encryption algorithm type, key length adjustment data, and encryption operation efficiency score; the device risk score data includes network connection security level, application behavior abnormality score, and device vulnerability scanning results; the mobile device encryption parameters include key update cycle, encryption strength level, and encryption protocol matching results.

[0014] As a further solution of the present invention, based on the mobile device operation information, user login data and device usage data are collected, the time and location information of the user login and device usage are identified, and the risk scores of multiple behavior patterns are calculated. The steps of obtaining user behavior identification data are specifically as follows:

[0015] S101: Based on the mobile device operation information, collect the user login time and location information of the mobile device, analyze the device usage frequency and application access records, and obtain user behavior statistics;

[0016] S102: Based on the user behavior statistical data, the time series data of the target user's login time and location information is analyzed to identify the user behavior pattern and obtain a behavior pattern analysis result;

[0017] S103: Based on the behavior pattern analysis results, risk scores of multiple user patterns are calculated to obtain user behavior identification data.

[0018] As a further solution of the present invention, based on the user behavior identification data, the steps of identifying unauthorized access attempts by analyzing abnormal login data and generating abnormal access identification information are specifically as follows:

[0019] S201: Based on the user behavior identification data, analyzing the user behavior identification data, identifying data records where login time and location deviate from a regular pattern, including login attempts at unusual times and locations, and obtaining abnormal login identification results;

[0020] S202: Based on the abnormal login identification result, by analyzing abnormal changes in access frequency and device type, identifying consistency between multiple login information and unauthorized access patterns, and obtaining unauthorized access analysis results;

[0021] S203: Based on the unauthorized access analysis result, taking into account the user's historical security records and real-time security configuration, unauthorized access attempts are identified and recorded to obtain abnormal access identification information.

[0022] As a further solution of the present invention, based on the abnormal access identification information, the real-time network conditions and device performance of the mobile device are evaluated, the sensitivity scores of various data are calculated, and the steps of obtaining the data risk level score are specifically as follows:

[0023] S301: Based on the abnormal access identification information, monitor the network bandwidth and delay indicators of multiple mobile devices in real time, evaluate the device operation performance, and generate real-time device performance data;

[0024] S302: Based on the real-time device performance data, using an information entropy algorithm and considering the data type, evaluating the sensitivity levels of multiple data that need to be encrypted, including personal information and financial data, and generating a data sensitivity score;

[0025] S303: Based on the data sensitivity score and in combination with the device performance information, the risk levels of various data are analyzed to generate a data risk level score.

[0026] As a further solution of the present invention, the information entropy algorithm is according to the formula:

[0027]

[0028] Calculate the sensitivity score of the data, where H is the information entropy value, X is the set of random variables, i is the index variable, n is the total number of data types, and w i is the weight coefficient of the i-th data type, x i represents the i-th data type, p(x i ) is the probability of the i-th data type being accessed, p represents the probability function, p(x i )logp(x i ) is the amount of information of the i-th data type.

[0029] As a further solution of the present invention, based on the data risk level scoring, the performance data of the encryption parameter configuration is analyzed through real-time monitoring and efficiency evaluation, and the encryption parameters are adjusted to optimize the efficiency and security of data encryption. The steps of generating encryption parameter adjustment information are specifically as follows:

[0030] S401: According to the data risk level score, collect device performance data in the encryption operation and record the encryption parameters used, including encryption speed and processing delay, and generate an encryption performance record;

[0031] S402: Based on the encryption performance record, evaluate the impact of various parameters on performance and security, analyze the types of encryption parameters that need to be adjusted, and generate encryption parameter analysis results;

[0032] S403: According to the encryption parameter analysis result, the encryption parameter key length and encryption level are adjusted in consideration of encryption efficiency and data security, and encryption parameter adjustment information is generated.

[0033] As a further solution of the present invention, based on the encryption parameter adjustment information, a security scan is performed on the network environment and application behavior of the mobile device to evaluate the security status of the network and interactive applications connected to the device, and the steps of obtaining the device risk score data are specifically as follows:

[0034] S501: Based on the encryption parameter adjustment information, network connection types and signal strength data of multiple mobile devices are collected in real time, network stability and transmission rate information are recorded, and network status evaluation data is obtained;

[0035] S502: Scan the permissions and behaviors of applications installed on the mobile device according to the network status assessment data, record the permission usage and background activity data of the applications, analyze the application risks, and generate application risk assessment results;

[0036] S503: Based on the application risk assessment result and in combination with the real-time network status, the security status of multiple mobile devices is assessed and the device risk level is calculated to obtain device risk score data.

[0037] As a further solution of the present invention, based on the device risk score data, combined with the risk score of the user behavior pattern and the sensitivity level of the data, the update frequency and encryption parameters of the target mobile device key are adjusted, and the steps of obtaining the encryption parameters of the mobile device are specifically as follows:

[0038] S601: Based on the device risk score data, combined with the risk score of the user behavior pattern and the sensitivity level of the data, the data encryption level required by the target mobile device is evaluated and calculated to obtain a risk impact assessment result;

[0039] S602: Based on the risk impact assessment result, according to the data protection requirements of various data sensitivity levels, the key update frequency and the data encryption level are adjusted, and an encryption parameter adjustment record is generated;

[0040] S603: Apply the encryption parameter adjustment record to update the encryption parameters of multiple mobile devices, adjust the key length and encryption algorithm configuration to match the security requirements, and obtain the encryption parameters of the mobile devices.

[0041] A cloud-based EMM encryption system, the cloud-based EMM encryption system is used to execute the above-mentioned cloud-based EMM encryption method, the system comprising:

[0042] The device log analysis module collects the user login time and location information of the mobile device based on the mobile device operation information, analyzes the device usage frequency and application access records, and generates behavioral statistics;

[0043] The access behavior analysis module identifies abnormal login attempts and abnormal device usage based on the behavior statistics, records unauthorized access behaviors, and obtains unauthorized usage records;

[0044] The sensitivity analysis module evaluates the network conditions and performance indicators of the mobile device based on the unauthorized use records, scores the data sensitivity, and generates a data sensitivity score;

[0045] The encryption configuration adjustment module monitors the performance parameters of encryption operations in real time based on the data sensitivity score, evaluates the efficiency and security of various encryption parameter configurations, and adjusts the key length and encryption algorithm to obtain encryption parameter optimization results;

[0046] The security scanning and analysis module scans the network environment and application behavior of the mobile device based on the encryption parameter optimization result, adjusts the key update frequency and encryption parameters in combination with the device risk score and the user behavior pattern risk score, and generates encryption parameters for the mobile device.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by real-time collection and analysis of user login data and device usage data, unauthorized access is identified, and risk scoring is performed on mobile devices, thereby improving the comprehensiveness and dynamism of data security management. In combination with real-time monitoring of user behavior patterns and device performance, encryption parameters are optimized using data sensitivity scoring to improve the efficiency and security of data encryption. By adjusting the update frequency and parameters of encryption keys, the risk of data leakage is reduced, the response speed to security threats is improved, the flexibility of enterprise data protection is enhanced, and business continuity is maintained. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a cloud-based EMM encryption method, comprising the following steps:

[0060] S1: Based on the mobile device operation information, collect user login data and device usage data, identify the time and location information of user login and device usage, calculate the risk scores of multiple behavior patterns, and obtain user behavior identification data;

[0061] S2: Based on user behavior identification data, unauthorized access attempts are identified by analyzing abnormal login data, and abnormal access identification information is generated;

[0062] S3: Based on abnormal access identification information, evaluate the real-time network conditions and device performance of mobile devices, calculate the sensitivity scores of various data, and obtain the data risk level score;

[0063] S4: Based on the data risk level score, through real-time monitoring and efficiency evaluation, analyze the performance data of encryption parameter configuration, adjust the encryption parameters to optimize the efficiency and security of data encryption, and generate encryption parameter adjustment information;

[0064] S5: Based on the encryption parameter adjustment information, perform a security scan on the network environment and application behavior of the mobile device, evaluate the security status of the network and interactive applications connected to the device, and obtain device risk score data;

[0065] S6: Based on the device risk score data, combined with the risk score of the user behavior pattern and the sensitivity level of the data, the update frequency and encryption parameters of the target mobile device key are adjusted to obtain the encryption parameters of the mobile device.

[0066] User behavior identification data includes login frequency information, login location information, and device type identification information. Abnormal access identification information includes unauthorized login time points, abnormal login source records, and abnormal login device information. Data risk level scoring includes the frequency of sensitive file access, data category sensitivity information, and the degree of matching between access behavior and risk patterns. Encryption parameter adjustment information includes encryption algorithm type, key length adjustment data, and encryption operation efficiency score. Device risk score data includes network connection security level, application behavior abnormality score, and device vulnerability scanning results. Mobile device encryption parameters include key update cycle, encryption strength level, and encryption protocol matching results.

[0067] See also Figure 2 Based on the mobile device operation information, the user login data and device usage data are collected, the time and location information of the user login and device usage are identified, and the risk scores of various behavior patterns are calculated. The specific steps for obtaining user behavior identification data are as follows:

[0068] S101: Based on the mobile device operation information, the user login time and location information of the mobile device is collected, the device usage frequency and application access records are analyzed, and the process of obtaining user behavior statistics is as follows;

[0069] In sub-step S101, based on the mobile device operation information, the login time and location are collected, the device usage frequency and application access records are analyzed, and the device information and user behavior data are pre-processed using data normalization and time series analysis methods, including data cleaning and outlier processing to ensure data quality. Time series analysis is performed on the user login time and location information, and the trend of login frequency and location changes is extracted using moving average and exponential smoothing techniques to analyze user behavior patterns. The formula is: z (t) = α z x z (t)+(1-α z )y z (t-1), where y z (t) represents the smoothed sequence value, x z (t) is the current observation value, α z is the smoothing coefficient, which represents the weight of new information and obtains user behavior statistics.

[0070] S102: Based on the user behavior statistical data, the time series data of the target user's login time and location information is analyzed to identify the user behavior pattern, and the specific process of obtaining the behavior pattern analysis result is as follows;

[0071] In sub-step S102, based on user behavior statistical data, time series analysis technology is used to analyze user login time and location, identify and predict user behavior patterns, and the time series data is analyzed by constructing an autoregressive moving average model. The formula is: in, is the predicted value at time point t, c z is a constant term, is the error term, is the autoregression coefficient, is the moving average coefficient, p 0 and q 0 are the orders of the model respectively, identifying user behavior patterns in different time periods and locations, providing a data basis for analyzing user behavior, and obtaining behavior pattern analysis results.

[0072] S103: Based on the behavior pattern analysis results, the risk scores of multiple user patterns are calculated to obtain the user behavior identification data. Specifically, the process is as follows;

[0073] In sub-step S103, based on the behavior pattern analysis results, logistic regression analysis is used to calculate the risk scores of different user patterns, and risk assessment is provided by associating user behavior patterns with potential risks. The formula is: Among them, p z is the probability of the target event, is the intercept, Corresponding factor The coefficient represents multiple dimensions of user behavior, including login frequency and location changes, which can assess risks and predict behavior trends, enhance security and personalized service capabilities, and obtain user behavior identification data.

[0074] See also Figure 3 Based on user behavior identification data, unauthorized access attempts are identified by analyzing abnormal login data. The specific steps for generating abnormal access identification information are as follows:

[0075] S201: Based on the user behavior identification data, the process of analyzing the user behavior identification data to identify data records where the login time and location deviate from the normal pattern, including login attempts at unconventional times and locations, and obtaining abnormal login identification results is as follows;

[0076] In sub-step S201, based on the user behavior recognition data, the login time and location data that deviate from the normal pattern are analyzed, and a clustering algorithm is used to mark login attempts at unconventional times and locations, including data points that are different from the user's daily behavior pattern. The formula is: Among them, D ct is the total number of abnormal data points, is the indicator function for deviation from the normal pattern, and Represents the data points of login time and location, N 1 is the total number of data points. Abnormal behaviors are identified by accumulating all login attempts that deviate from the normal pattern to generate abnormal login identification results.

[0077] S202: Based on the abnormal login identification result, by analyzing the abnormal changes in access frequency and device type, identifying the consistency of multiple login information and unauthorized access patterns, and obtaining the unauthorized access analysis result, the specific process is as follows;

[0078] In sub-step S202, based on the abnormal login identification results, the abnormal changes in access frequency and device type are analyzed, and a multivariate statistical analysis method is applied to identify the consistency of login information and unauthorized access patterns. The formula is: in, is the consistency measure for unauthorized access, is the weight of each variable, is the observed frequency or device type, is the normal value of the corresponding variable, M 1 is the total number of variables. The association between each variable and the unauthorized access mode is evaluated by weighted summation to generate the unauthorized access analysis results.

[0079] S203: Based on the unauthorized access analysis results, the user's historical security records and real-time security configuration are considered, and unauthorized access attempts are identified and recorded, and the specific process of obtaining abnormal access identification information is as follows;

[0080] In sub-step S203, the unauthorized access analysis results are used to evaluate and identify the user's historical security records and real-time security configuration, and the decision tree algorithm is used for pattern recognition and decision making. The formula is: in, As a result of decision making, is the decision function, To record or configure the weight of parameters for security, is the corresponding parameter value, K 1 is the total number of parameters. The decision result generated by the decision function after weighted summation of each parameter is used to identify unauthorized access attempts and obtain abnormal access identification information.

[0081] See also Figure 4 Based on the abnormal access identification information, the real-time network conditions and device performance of the mobile device are evaluated, and the sensitivity scores of various data are calculated. The specific steps to obtain the data risk level score are as follows:

[0082] S301: Based on abnormal access identification information, the network bandwidth and delay indicators of multiple mobile devices are monitored in real time, the device operation performance is evaluated, and the process of generating real-time device performance data is specifically as follows;

[0083] In sub-step S301, based on the abnormal access identification information, the network bandwidth and delay indicators of multiple mobile devices are monitored, including data collection and real-time processing, and the network performance of the device is evaluated. The formula is: in, Represents the performance evaluation value of the device, For m 2 The network bandwidth of each device N is the corresponding network delay. 2 For the total number of monitored devices, it provides performance assessment by averaging bandwidth and latency metrics across multiple devices, generating real-time device performance data.

[0084] S302: Based on the real-time device performance data, an information entropy algorithm is used to consider the data type and evaluate the sensitivity levels of multiple data that need to be encrypted, including personal information and financial data. The specific process of generating data sensitivity scores is as follows;

[0085] In sub-step S302, based on real-time device performance data, an information entropy algorithm is used to evaluate the sensitivity level of data that needs to be encrypted, and the information entropy evaluation sensitivity of differentiated data types is calculated, including personal information and financial data, to optimize decision data encryption and protection strategies and generate data sensitivity scores.

[0086] Information entropy algorithm, according to the formula:

[0087]

[0088] Calculate the sensitivity score of the data, where H is the information entropy value, which is used to measure the total score of data sensitivity, X is a set of random variables, representing different types of sensitive data, i is an index variable, which is used to indicate the location of the current data type, n is the total number of data types, which indicates the total number of data types considered in the system, and w i is the weight coefficient of the i-th data type, which adjusts the influence of different data types in the total score. i represents the i-th data type, which is used to distinguish different data categories, including personal information and financial data. i ) is the probability of the i-th data type being accessed, which is used to measure its frequency of occurrence. p represents the probability function, which is used to calculate the probability of the target event. p(x i )logp(x i ) is the amount of information of the i-th data type, which is related to the probability of being accessed.

[0089] The specific execution process of the formula is as follows:

[0090] The monitoring system collects different types of data usage statistics and obtains the access frequency of each data type as p(x i ), according to the business impact of the data type and historical leakage cases, the risk assessment model is used to obtain the weight w of each data type. i , substitute the number into the formula and calculate the amount of information p(x i )logp(x i ), and multiply it by the corresponding weight w i , all results are accumulated to obtain the data sensitivity score H(X), which is used to adjust data protection parameters.

[0091] S303: Based on the data sensitivity score, combined with the equipment performance information, the risk level of various data is analyzed, and the process of generating the data risk level score is specifically as follows;

[0092] In sub-step S303, based on the data sensitivity score and combined with the equipment performance information, data risk level analysis is performed, and a weighted risk scoring model is used to integrate the sensitivity score and equipment performance data to identify data risks. The formula is: in, Score the risk level of the data. and are the weights of sensitivity score and equipment performance data, To score the sensitivity, Rating the performance of the device, R 2 For the type of data and number of devices being assessed, a sensitivity and performance score is calculated by weighted summation to generate a data risk level.

[0093] See also Figure 5 ,Based on the data risk level score, through real-time monitoring and efficiency evaluation, the performance data of encryption parameter configuration is analyzed, and encryption parameters are adjusted to optimize the efficiency and security of data encryption. The specific steps for generating encryption parameter adjustment information are as follows:

[0094] S401: According to the data risk level score, the device performance data in the encryption operation is collected and the encryption parameters used are recorded, including the encryption speed and processing delay. The specific process of generating the encryption performance record is as follows;

[0095] In sub-step S401, according to the data risk level score, the device performance data in the encryption operation is collected and recorded, including encryption speed and processing delay, for evaluation and optimization of the encryption process. The formula is: in, represents the average evaluation value of encryption performance, and Respectively represent the nth 3 The encryption speed and processing delay of each device, K 3 For the total number of devices, an encryption performance record is generated by calculating the average encryption speed and processing latency of the devices.

[0096] S402: Based on the encryption performance record, evaluate the impact of various parameters on performance and security, analyze the encryption parameter types that need to be adjusted, and generate the encryption parameter analysis results. The specific process is as follows;

[0097] In sub-step S402, based on the encryption performance record, the impact of differentiated encryption parameters on system performance and security is evaluated, using a parameter impact analysis model, the formula is: in, Represents the comprehensive evaluation value of the impact of encryption parameters, For the s 3 The weight of the parameters, is the performance or safety impact value of the corresponding parameter, S 3 The total number of parameters is evaluated through weighted summation to contribute to the performance and security of each parameter, provide data support for adjusting encryption parameters, and generate encryption parameter analysis results.

[0098] S403: According to the result of the encryption parameter analysis, the encryption efficiency and data security are considered, and the key length and encryption level of the encryption parameter are adjusted. The specific process of generating the encryption parameter adjustment information is as follows;

[0099] In sub-step S403, based on the encryption parameter analysis results, encryption efficiency and data security are considered to adjust the encryption parameters, optimize the encryption parameters and ensure data security and encryption efficiency. The formula is: Among them, C bj represents the adjusted key performance and security score, and are the weights of key length and encryption level, respectively. and For the corresponding performance and safety scores, T 3 For the number of parameter types evaluated, encryption parameters are adjusted by considering the impact of key length and encryption level on performance and security, and encryption parameter adjustment information is generated.

[0100] See also Figure 6 Based on the encryption parameter adjustment information, the network environment and application behavior of the mobile device are scanned for security, and the security status of the network and interactive applications connected to the device is evaluated. The specific steps for obtaining the device risk score data are as follows:

[0101] S501: Based on the encryption parameter adjustment information, the network connection type and signal strength data of multiple mobile devices are collected in real time, the network stability and transmission rate information are recorded, and the process of obtaining the network status evaluation data is specifically as follows;

[0102] In sub-step S501, based on the encryption parameter adjustment information, the network connection type and signal strength data of multiple mobile devices are collected to evaluate the overall health of the network. The formula is: in, represents the network quality index, Represents the u 4 The signal strength of each device, is the network stability coefficient of the device, Z 4 For the total number of devices to be evaluated, the network quality index is calculated by performing logarithmic transformation on the signal strength and combining it with the network stability coefficient. This reflects the impact of the network environment on the encryption performance of mobile devices and obtains the network status evaluation data.

[0103] S502: Scan the permissions and behaviors of applications installed on the mobile device according to the network status assessment data, record the permission usage and background activity data of the applications, analyze the application risks, and generate the application risk assessment results.

[0104] In sub-step S502, the network status assessment data is used to scan the permissions and behaviors of device applications, including the permission configuration and background activities of the applications, to identify security vulnerabilities and improper behaviors. The formula is: in, Indicates the total value of application risk assessment, For c 4 The permission level of each application. is the safety factor of background activities, C 4 For the total number of scanned applications, a risk assessment value is calculated for each application by combining the square of the permission level with the exponential negative function of the background activity, the security risk is assessed, and the application risk assessment result is generated.

[0105] S503: Based on the application risk assessment result and in combination with the real-time network status, the security status of multiple mobile devices is assessed and the device risk level is calculated to obtain the device risk score data. Specifically, the process is as follows;

[0106] In sub-step S503, based on the application risk assessment results and combined with the real-time network status, the device security status is assessed, including analyzing the application risk and network quality of each device, determining the overall security status of the device, and performing weighted calculation on the application risk assessment results and network quality of each device to assess the comprehensive impact on the device security status. The formula is: in, Indicates the comprehensive risk level score of the device. is the weight factor of application risk, is the weight factor of network quality, and Represents the vth 4 The application risk score and network quality score of each device, V 4 The total number of devices involved in the assessment is calculated by calculating the maximum value of the weighted sum of each device to determine the highest security risk level in the device, identify and prioritize risky devices, and obtain device risk score data.

[0107] See also Figure 7 Based on the device risk score data, combined with the risk score of the user behavior pattern and the sensitivity level of the data, the update frequency and encryption parameters of the target mobile device key are adjusted. The specific steps for obtaining the encryption parameters of the mobile device are as follows:

[0108] S601: Based on the device risk score data, combined with the risk score of the user behavior pattern and the sensitivity level of the data, the data encryption level required by the target mobile device is evaluated and calculated, and the process of obtaining the risk impact assessment result is specifically as follows;

[0109] In sub-step S601, based on the device risk score data, the risk score of the user behavior pattern and the sensitivity level of the data, the data encryption level required by the target mobile device is determined. By considering multiple levels of risk factors, the data encryption level is ensured to match the security threat. The formula is: Among them, E gi represents the comprehensive risk assessment value, Score the risk of the device. is the data sensitivity level, and are the weight factors of user behavior risk and data sensitivity, respectively, 5 The total number of devices or data categories to be assessed, the interaction between risk and sensitivity is considered, the appropriate encryption level is determined, and the risk impact assessment results are obtained.

[0110] S602: Based on the risk impact assessment results and in accordance with the data protection requirements of various data sensitivity levels, the key update frequency and data encryption level are adjusted, and the specific process of generating encryption parameter adjustment records is as follows;

[0111] In sub-step S602, according to the risk impact assessment results, the key update frequency and data encryption level are adjusted to match the protection requirements of different data sensitivity levels. The formula is: in, Adjust decision values ​​for encryption parameters, The risk impact assessment results for each data category are: is the adjustment factor, which indicates the sensitivity to the adjustment of encryption level or update frequency, Z 5 For the total number of data categories involved in the decision-making, by evaluating the risk assessment results of each type of data, determining the key update frequency and encryption level adjustment and enhancing data protection, and generating encryption parameter adjustment records.

[0112] S603: Apply the encryption parameter adjustment record, update the encryption parameters of multiple mobile devices, adjust the key length and encryption algorithm configuration to match the security requirements, and obtain the encryption parameters of the mobile devices.

[0113] In sub-step S603, based on the encryption parameter adjustment record, the key length and encryption algorithm configuration of multiple mobile devices are updated and adjusted to ensure that the encryption settings can meet the actual security requirements and adapt to the risk environment. The formula is: in, Set values ​​for encryption parameters, is the adjusted encryption parameter decision value, Adjust the weights for the parameters, X 5 For the total number of devices evaluated, the optimal encryption configuration is selected by calculating the weighted maximum value to optimize data security and device performance and obtain the encryption parameters of the mobile device.

[0114] See also Figure 8 , a cloud-based EMM encryption system, the cloud-based EMM encryption system is used to execute the above-mentioned cloud-based EMM encryption method, the system includes:

[0115] The device log analysis module collects the user login time and location information of the mobile device based on the mobile device operation information, analyzes the device usage frequency and application access records, and generates behavioral statistics;

[0116] The access behavior analysis module identifies abnormal login attempts and device usage based on behavioral statistical data, records unauthorized access behaviors, and obtains unauthorized use records;

[0117] The sensitivity analysis module evaluates the network conditions and performance indicators of mobile devices based on unauthorized use records, scores the data sensitivity, and generates a data sensitivity score;

[0118] The encryption configuration adjustment module monitors the performance parameters of encryption operations in real time based on data sensitivity scores, evaluates the efficiency and security of various encryption parameter configurations, and adjusts the key length and encryption algorithm to obtain encryption parameter optimization results;

[0119] The security scanning and analysis module scans the network environment and application behavior of the mobile device based on the encryption parameter optimization results, adjusts the key update frequency and encryption parameters based on the device risk score and user behavior pattern risk score, and generates encryption parameters for the mobile device.

[0120] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A cloud-based EMM encryption method, characterized in that: The following steps are involved: Based on the mobile device operation information, collect user login data and device usage data, identify the time and location information of user login and device usage, calculate the risk scores of various behavior patterns, and obtain user behavior identification data; Based on the user behavior identification data, identifying unauthorized access attempts by analyzing abnormal login data, and generating abnormal access identification information; Based on the abnormal access identification information, the real-time network conditions and device performance of the mobile device are evaluated, sensitivity scores of various data are calculated, and data risk level scores are obtained; Based on the data risk level score, the performance data of the encryption parameter configuration is analyzed through real-time monitoring and efficiency evaluation, the encryption parameters are adjusted to optimize the efficiency and security of data encryption, and encryption parameter adjustment information is generated; Based on the encryption parameter adjustment information, a security scan is performed on the network environment and application behavior of the mobile device to evaluate the security status of the network and interactive applications connected to the device to obtain device risk score data; Based on the device risk score data, combined with the risk score of the user behavior pattern and the sensitivity level of the data, the update frequency and encryption parameters of the target mobile device key are adjusted to obtain the mobile device encryption parameters.

2. The cloud-based EMM encryption method according to claim 1, characterized in that: The user behavior identification data includes login frequency information, login location information, and device type identification information; the abnormal access identification information includes unauthorized login time points, abnormal login source records, and abnormal login device information; the data risk level score includes the frequency of sensitive file access, data category sensitivity information, and the degree of matching between access behavior and risk patterns; the encryption parameter adjustment information includes the encryption algorithm type, key length adjustment data, and encryption operation efficiency score; the device risk score data includes the network connection security level, application behavior abnormality score, and device vulnerability scanning results; the mobile device encryption parameters include the key update cycle, encryption strength level, and encryption protocol matching results.

3. The cloud-based EMM encryption method according to claim 1, characterized in that: Based on the mobile device operation information, collect user login data and device usage data, identify the time and location information of user login and device usage, calculate the risk scores of various behavior patterns, and obtain user behavior identification data in the following steps: Based on the mobile device operation information, collect the user login time and location information of the mobile device, analyze the device usage frequency and application access records, and obtain user behavior statistics; Based on the user behavior statistical data, the user behavior pattern is identified by analyzing the time series data of the target user's login time and location information to obtain a behavior pattern analysis result; Based on the behavior pattern analysis results, risk scores of multiple user patterns are calculated to obtain user behavior identification data.

4. The cloud-based EMM encryption method according to claim 1, characterized in that: Based on the user behavior identification data, the steps of identifying unauthorized access attempts by analyzing abnormal login data and generating abnormal access identification information are specifically as follows: Based on the user behavior identification data, analyzing the user behavior identification data to identify data records where login time and location deviate from a regular pattern, including login attempts at unusual times and locations, to obtain abnormal login identification results; Based on the abnormal login identification result, by analyzing the abnormal changes in access frequency and device type, the consistency of multiple login information and unauthorized access mode is identified to obtain the unauthorized access analysis result; Based on the unauthorized access analysis results, taking into account the user's historical security records and real-time security configuration, unauthorized access attempts are identified and recorded to obtain abnormal access identification information.

5. The cloud-based EMM encryption method according to claim 1, characterized in that: Based on the abnormal access identification information, the real-time network conditions and device performance of the mobile device are evaluated, and the sensitivity scores of various data are calculated to obtain the data risk level scores. Specifically, the steps are as follows: Based on the abnormal access identification information, monitor the network bandwidth and latency indicators of multiple mobile devices in real time, evaluate the device operation performance, and generate real-time device performance data; Based on the real-time device performance data, an information entropy algorithm is used to consider the data type, evaluate the sensitivity levels of multiple data that need to be encrypted, including personal information and financial data, and generate a data sensitivity score; Based on the data sensitivity score and combined with the equipment performance information, the risk level of various data is analyzed to generate a data risk level score.

6. The cloud-based EMM encryption method according to claim 5, characterized in that: The information entropy algorithm is based on the formula: Calculate the sensitivity score of the data, where H is the information entropy value, X is the set of random variables, i is the index variable, n is the total number of data types, and w i is the weight coefficient of the i-th data type, x i represents the i-th data type, p(x i ) is the probability of the i-th data type being accessed, p represents the probability function, p(x i )logp(x i is the amount of information of the i-th data type.

7. The cloud-based EMM encryption method according to claim 1, characterized in that: Based on the data risk level score, the performance data of the encryption parameter configuration is analyzed through real-time monitoring and efficiency evaluation, and the encryption parameters are adjusted to optimize the efficiency and security of data encryption. The steps of generating encryption parameter adjustment information are specifically as follows: Based on the data risk level score, collect device performance data in encryption operations and record encryption parameters used, including encryption speed and processing delay, to generate encryption performance records; Based on the encryption performance record, evaluate the impact of various parameters on performance and security, analyze the types of encryption parameters that need to be adjusted, and generate encryption parameter analysis results; According to the encryption parameter analysis result, the encryption parameter key length and encryption level are adjusted in consideration of encryption efficiency and data security, and encryption parameter adjustment information is generated.

8. The cloud-based EMM encryption method according to claim 1, characterized in that: Based on the encryption parameter adjustment information, the network environment and application behavior of the mobile device are scanned for security, the security status of the network and interactive applications connected to the device are evaluated, and the steps of obtaining the device risk score data are specifically as follows: Based on the encryption parameter adjustment information, network connection types and signal strength data of multiple mobile devices are collected in real time, network stability and transmission rate information are recorded, and network status evaluation data is obtained; Based on the network status assessment data, scan the permissions and behaviors of applications installed on the mobile device, record the permission usage and background activity data of the applications, analyze the application risks, and generate application risk assessment results; Based on the application risk assessment results and combined with the real-time network status, the security status of multiple mobile devices is evaluated and the device risk level is calculated to obtain device risk score data.

9. The cloud-based EMM encryption method according to claim 1, characterized in that: Based on the device risk score data, combined with the risk score of the user behavior pattern and the sensitivity level of the data, the update frequency and encryption parameters of the target mobile device key are adjusted, and the steps of obtaining the encryption parameters of the mobile device are specifically as follows: Based on the device risk score data, combined with the risk score of the user behavior pattern and the sensitivity level of the data, the data encryption level required by the target mobile device is evaluated and calculated to obtain a risk impact assessment result; Based on the risk impact assessment results, according to the data protection requirements of various data sensitivity levels, the key update frequency and the data encryption level are adjusted, and an encryption parameter adjustment record is generated; The encryption parameter adjustment record is applied to update the encryption parameters of multiple mobile devices, adjust the key length and encryption algorithm configuration to match the security requirements, and obtain the encryption parameters of the mobile devices.

10. A cloud-based EMM encryption system, characterized in that: According to any one of claims 1 to 9, the cloud-based EMM encryption method comprises: The device log analysis module collects the user login time and location information of the mobile device based on the mobile device operation information, analyzes the device usage frequency and application access records, and generates behavioral statistics; The access behavior analysis module identifies abnormal login attempts and abnormal device usage based on the behavior statistics, records unauthorized access behaviors, and obtains unauthorized usage records; The sensitivity analysis module evaluates the network conditions and performance indicators of the mobile device based on the unauthorized use records, scores the data sensitivity, and generates a data sensitivity score; The encryption configuration adjustment module monitors the performance parameters of encryption operations in real time based on the data sensitivity score, evaluates the efficiency and security of various encryption parameter configurations, and adjusts the key length and encryption algorithm to obtain encryption parameter optimization results; The security scanning and analysis module scans the network environment and application behavior of the mobile device based on the encryption parameter optimization result, adjusts the key update frequency and encryption parameters in combination with the device risk score and the user behavior pattern risk score, and generates encryption parameters for the mobile device.

Images