A neural network-based data security early warning system and method

Abstract

This invention discloses a data security early warning system and method based on neural networks, including a permission classification unit, an existing data retrieval unit, a mobile phone background monitoring unit, an unauthorized data theft early warning unit, and a permission level monitoring unit. The permission classification unit is used to classify mobile phone information into different types of mobile phone information blocks based on permissions. The existing data retrieval unit is used to monitor the data retrieval frequency of mobile phone information blocks for different purposes. The mobile phone background monitoring unit is used to monitor the dynamic operation of mobile phone information blocks. The permission level monitoring unit is used to monitor the real-time permission status of the storage space where the mobile phone information blocks are located. The unauthorized data theft early warning unit is used to provide data retrieval security warnings based on the usage of mobile phone information blocks. This invention sets matching data retrieval security warning specifications for mobile phone information based on multiple factors to achieve precise protection of mobile phone information data retrieval.

Description

Technical Field

[0001] This invention relates to the field of mobile phone data security, and in particular to a data security early warning system and method based on neural networks. Background Technology

[0002] On many mainstream smartphone platforms today, application permissions are pre-defined during the programming phase and cannot be changed. Users can only choose to accept all permissions or refuse installation, a very rigid model that prevents users from flexibly allocating permissions to each application according to their specific needs. Furthermore, for user-defined data, only default permissions can be used, and permissions cannot be assigned according to the user's expectations.

[0003] Meanwhile, while it's currently feasible for each application to maintain its own permission list, as the number of applications increases and mobile phone functions become more sophisticated, further segmentation of permissions is an inevitable trend. This "tailor-made" permission management and verification will become redundant and chaotic. Relying solely on a single permission list for verification is also not very secure.

[0004] To protect the data security on mobile phones, it is particularly important to limit the permissions of different applications. Therefore, this invention proposes a data security early warning system and method based on neural networks. Summary of the Invention

[0005] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a data security early warning system and method based on neural networks.

[0006] A data security early warning system based on neural networks includes a permission classification unit, an existing data retrieval unit, a mobile phone background monitoring unit, an unauthorized data theft early warning unit, a permission level monitoring unit, an early warning APP, a mobile phone information integration unit, and a personal cloud data center. The permission classification unit categorizes mobile phone information into different types of information blocks for different purposes. The existing data retrieval unit is connected to a database unit, which stores personal privacy data from these information blocks and sends it to the existing data retrieval unit. The existing data retrieval unit monitors the data retrieval frequency of these information blocks and sends security early warning pop-ups to the personal cloud data center. The personal cloud data center sets data retrieval security early warning periods for these information blocks and sets different pop-up frequency cycles within these periods based on the security early warning pop-ups.

[0007] The mobile information integration unit is used to integrate dynamic operation data of mobile information blocks for different purposes and dynamic permission data of the storage space where the mobile information blocks are located during the data call security warning period, and send them to the personal cloud data center; the personal cloud data center also stores the standardized operation data and standardized permission data of the mobile information blocks; the personal cloud data center sends the dynamic operation data and standardized operation data to the mobile background monitoring unit, and the personal cloud data center sends the dynamic permission data and standardized permission data to the permission level monitoring unit;

[0008] The mobile phone background monitoring unit is used to monitor the dynamic operation of the mobile phone information block and send the obtained operation mutation value of the mobile phone information block to the personal cloud data center; the permission level monitoring unit is used to monitor the real-time permission status of the storage space where the mobile phone information block is located and feed back the permission mutation value of the storage space where the mobile phone information block is located to the personal cloud data center. The personal cloud data center sends the operation mutation value of the mobile phone information block and the permission mutation value of the storage space where the mobile phone information block is located to the unauthorized data theft early warning unit.

[0009] The personal cloud data center also sets a data access security warning value range for mobile phone information blocks based on the security warning pop-up and sends it to the unauthorized data theft warning unit; the unauthorized data theft warning unit is used to issue data access security warnings for the use of mobile phone information blocks, and generates mobile phone data access warning signals or mobile phone normal operation signals.

[0010] Furthermore, the monitoring process of the existing data retrieval unit is as follows:

[0011] The data call frequency of the mobile information block is obtained, and then the specific date of each data call of the mobile information block is obtained. The unit period of adjacent specific dates of data calls is calculated to obtain different data call time intervals. The covariance of different data call time intervals is calculated to obtain the data call time fluctuation pattern of the mobile information block.

[0012] The monitoring unit period for each data call of the mobile phone information is obtained, and the monitoring security coefficient of the mobile phone information block is obtained by convolution operation of the data call frequency during each data call.

[0013] Calculate the existing data retrieval value of the mobile phone information block;

[0014] The existing data retrieval value is matched with the existing data retrieval range to determine whether the security warning pop-up of the mobile information block is a security warning pop-up of a non-compliant APP, a security warning pop-up of a dangerous APP, or a security warning pop-up of a compliant APP.

[0015] Furthermore, the existing data retrieval value is directly proportional to the security warning pop-up. The larger the existing data retrieval value, the higher the security warning pop-up. The level of the security warning pop-up for compliant apps is lower than that for dangerous apps, and the level of the security warning pop-up for dangerous apps is lower than that for non-compliant apps.

[0016] Furthermore, the monitoring process of the mobile phone background monitoring unit is as follows:

[0017] Get the dynamic data call volume and dynamic protection count of the mobile information block during different pop-up cycles;

[0018] Match the dynamic data call volume of different pop-up periods with the standard data call volume range, match the dynamic protection number of different pop-up periods with the standard protection number range, mark pop-up periods that are not in the standard data call volume range as non-standard call periods, and mark pop-up periods that are not in the standard protection number range as non-standard protection periods.

[0019] Count the number of unconventional call cycles and the number of unconventional protection cycles;

[0020] The runtime mutation value of the mobile phone information block is calculated.

[0021] Furthermore, the monitoring process of the permission level monitoring unit is as follows:

[0022] Get real-time mobile phone temperature data and running speed per unit time of the storage space where the mobile phone information block is located during different pop-up periods;

[0023] The real-time mobile phone temperature data of different pop-up periods is matched with the standard mobile phone temperature data range, the unit time running speed of different pop-up periods is matched with the standard running speed range, the pop-up periods that are not in the standard mobile phone temperature data range are marked as abnormal temperature periods, and the pop-up periods that are not in the standard running speed range are marked as abnormal running speed periods.

[0024] Count the number of abnormal temperature cycles and the number of abnormal operating speed cycles;

[0025] Calculate the permission mutation value of the storage space where the mobile phone information block is located.

[0026] Furthermore, the working process of the unauthorized data theft early warning unit is as follows:

[0027] Obtain the runtime mutation value of the mobile information block and the permission mutation value of the storage space where the mobile information block is located;

[0028] Calculate the data retrieval security warning value for mobile phone information blocks;

[0029] A security alert pop-up window is generated to retrieve mobile phone information blocks. Based on the security alert pop-up window, the data retrieval security alert value range corresponding to the mobile phone information blocks is obtained.

[0030] When the data access security warning value is within the data access security warning value range corresponding to the mobile phone information block, a mobile phone data access warning signal is generated.

[0031] If the data call security warning value is not within the data call security warning value range corresponding to the mobile phone information block, a normal mobile phone operation signal is generated.

[0032] The unauthorized data theft early warning unit will send a mobile phone data access warning signal or a normal mobile phone operation signal back to the personal cloud data center.

[0033] Furthermore, if the personal cloud data center receives a signal indicating that the phone is functioning normally, then the phone will operate normally.

[0034] If the personal cloud data center receives a mobile data access warning signal, it will send the warning signal to the warning APP corresponding to the mobile information block. After receiving the mobile data access warning signal, the warning APP will upgrade its permissions and restrict the permissions of other application APPs.

[0035] By using convolutional neural networks to build a deep learning model based on the results of each application's permission restrictions, the response speed of the warning app can be improved.

[0036] The deep learning model is established using the following expression:

[0037] E e =GE e-1 +H e ·α e

[0038] Among them, E e E represents the current permission scope of the application. e-1 This represents the permission scope of the application (APP) at the previous step, G represents the permission factor (a constant), He represents the factor affecting data retrieval changes, and α... e This indicates the data change value retrieved by the application at the current moment.

[0039] A data security early warning method based on neural networks, the method comprising:

[0040] A1, the permission classification unit categorizes mobile phone information into different permission-specific mobile phone information blocks. Using the existing data retrieval unit, it monitors the data retrieval frequency of these blocks and sends security alert pop-ups to the personal cloud data center.

[0041] A2, the personal cloud data center sets the data access security warning period for the mobile information block and sets different frequency pop-up cycles within the data access security warning period based on the security warning pop-up;

[0042] A3, the mobile information integration unit integrates dynamic operation data of mobile information blocks for different purposes and dynamic permission data of the storage space where the mobile information blocks are located during the data call security warning period. The personal cloud data center also stores the standardized operation data and standardized permission data of the mobile information blocks.

[0043] A4. The mobile phone background monitoring unit monitors the dynamic operation of the mobile phone information block and sends the abnormal operation value of the mobile phone information block to the unauthorized data theft early warning unit.

[0044] A5 monitors the real-time permission status of the storage space where the mobile information block is located through the permission level monitoring unit, and sends the permission mutation value of the storage space where the mobile information block is located to the unauthorized data theft early warning unit.

[0045] A6, Personal cloud data center combines security alert pop-up window to set data call security alert value range for mobile information block and send it to unauthorized data theft alert unit;

[0046] A7, the unauthorized data theft warning unit, provides data access security warnings based on the usage of mobile phone information blocks, generating mobile phone data access warning signals or mobile phone normal operation signals.

[0047] A8 uses a convolutional neural network to build a deep learning model based on the results of each application's permission restrictions, thereby improving the response speed of warning apps.

[0048] Beneficial effects:

[0049] This invention divides mobile phone information into different-purpose mobile phone information blocks through a permission classification unit. Then, it uses an existing data retrieval unit to monitor the data retrieval frequency of these blocks, generating security alert pop-ups that are fed back to a personal cloud data center. The personal cloud data center sets data retrieval security alert periods for these blocks and sets different pop-up frequency cycles within these periods based on the security alert pop-ups. The personal cloud data center, combined with the security alert pop-ups, sets data retrieval security alert value ranges for these blocks and sends them to an unauthorized data theft alert unit. Finally, a mobile phone background monitoring unit monitors the dynamic operation of these mobile phone information blocks to obtain the data retrieval information. The system sends the abnormal operation value to the unauthorized data theft early warning unit. The permission level monitoring unit monitors the real-time permission status of the storage space where the mobile information block is located, obtains the permission abnormality value of the storage space where the mobile information block is located, and sends it to the unauthorized data theft early warning unit. Finally, the unauthorized data theft early warning unit issues a data call security warning based on the usage of the mobile information block, generating a mobile data call warning signal or a normal mobile operation signal. This invention sets matching data call security warning specifications for mobile information based on multiple factors, sets permission ranges for different applications, and uses convolutional neural networks to improve protection speed, thereby achieving precise protection of mobile information data calls and effectively protecting mobile data security. Attached Figure Description

[0050] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0051] Figure 1 This is a diagram showing the system module composition of the present invention;

[0052] Figure 2 This is a flowchart of the data call frequency monitoring process of the present invention;

[0053] Figure 3 This is a flowchart illustrating the dynamic operation monitoring of the mobile phone information block according to the present invention.

[0054] Figure 4 This is a flowchart illustrating the real-time permission monitoring process for the storage space where the mobile phone information block is located, as described in this invention.

[0055] Figure 5 The following is a flowchart of the method of the present invention. Detailed Implementation

[0056] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] like Figure 1 As shown, a data security early warning system based on neural networks includes a permission classification unit, an existing data retrieval unit, a mobile phone background monitoring unit, an unauthorized data theft early warning unit, a permission level monitoring unit, an early warning APP, a mobile phone information integration unit, and a personal cloud data center.

[0058] In practice, the warning app allows the phone owner to input personal information to set permissions and sends the personal information to the personal cloud data center; the permission settings are based on the phone owner's usage habits and personal preferences.

[0059] Specifically, the permission classification unit is used to classify mobile phone information into different permission categories and divide it into mobile phone information blocks for different purposes. In actual division, mobile phone information blocks can be divided according to the mobile phone station area, or according to the community or street. The divided mobile phone information lines can be assigned to the warning APP with the corresponding work number.

[0060] The existing data retrieval unit is connected to the database unit, which is used to store personal privacy data of mobile phone information blocks for different purposes and send the personal privacy data to the existing data retrieval unit.

[0061] It is necessary to specify the data call frequency of the mobile information block, as well as the specific dates and monitoring unit cycles for the corresponding data calls;

[0062] The existing data retrieval unit is used to monitor the frequency of data retrieval for mobile phone information blocks for different purposes. The monitoring process is as follows:

[0063] The mobile phone information blocks are marked, and the storage space is allocated based on the mobile phone user's usage habits, the number of installed apps, and the storage memory size using a convolutional neural network to establish a mobile phone information classification function.

[0064] A mobile phone information classification function is established using a convolutional neural network, with the following expression:

[0065] q ok =l ok +f1×rand()×(W ok -d ok )+f2×rand()×(W hk -dok )

[0066] Where, q ok Indicates the number of categories in the mobile phone information classification, l ok The boundary constants for classifying mobile phone information, determined by the number of installed apps and storage memory size, are: f1 represents the classification coefficient based on mobile phone user habits, rand() represents a random function, and d... ok W represents an unclassified set of mobile phone information. ok f2 represents the amount of mobile phone information already present in the training set, f2 represents the classification coefficient of the test set, and W represents the number of mobile phones in the training set. hk This indicates the amount of mobile phone information already present in the test set.

[0067] like Figure 2 As shown, the data call frequency of the mobile phone information block is obtained, and the data call frequency is marked as A;

[0068] Obtain the specific date of each data call in the mobile information block, calculate the unit period of adjacent specific data call dates to obtain different data call time intervals, and calculate the covariance of different data call time intervals to obtain the data call time fluctuation pattern B of the mobile information block;

[0069] The monitoring unit period for each data call of the mobile phone information is obtained, and the monitoring security coefficient C of the mobile phone information block is obtained by convolution operation of the data call frequency in each monitoring unit period.

[0070] Using formula The existing data retrieval value D of the mobile phone information block is calculated; where ε and δ are both proportional matrices composed of different factors;

[0071] When D < W, the security warning pop-up in the mobile information block is a security warning pop-up for a non-compliant APP;

[0072] When W≤D<S, the security warning pop-up in the mobile phone information block is a security warning pop-up for dangerous apps;

[0073] When S≤D, the security warning pop-up of the mobile information block is a compliant APP security warning pop-up; where W and S are existing data retrieval intervals composed of different factors, and W<S.

[0074] Understandably, the existing data retrieval value is directly proportional to the security warning pop-up. The larger the existing data retrieval value, the higher the security warning pop-up. That is, the level of the security warning pop-up of a compliant APP is lower than the level of the security warning pop-up of a dangerous APP, and the level of the security warning pop-up of a dangerous APP is lower than the level of the security warning pop-up of a non-compliant APP.

[0075] The existing data retrieval unit feeds back the security warning pop-up of the mobile phone information block to the personal cloud data center. The personal cloud data center is used to set the data retrieval security warning period of the mobile phone information block, and to set different frequency pop-up cycles within the data retrieval security warning period according to the security warning pop-up.

[0076] Understandably, when the mobile information block is a security warning pop-up for a compliant app, the number of pop-up cycles within the data access security warning period is Q. JK When the mobile phone information block displays a security warning pop-up for a dangerous app, the number of pop-up cycles within the data retrieval security warning period is Q. JF When the mobile information block contains a security warning pop-up for a non-compliant app, the number of pop-up cycles within the data retrieval security warning period is Q. JP Q JK Q JF and Q JP All are positive integers, and Q JK JF JP In specific implementation, Q JK The value of Q can be 5 times per week. JF The value of Q can be 20 times per week. JP The value can be 40 times per week;

[0077] The mobile information integration unit is used to integrate the dynamic operation data of mobile information blocks for different purposes and the dynamic permission data of the storage space where the mobile information blocks are located during the data access security warning period, and send the dynamic operation data and dynamic permission data to the personal cloud data center; the personal cloud data center also stores the standard operation data and standard permission data of the mobile information blocks; the personal cloud data center sends the dynamic operation data and standard operation data to the mobile background monitoring unit, and the personal cloud data center sends the dynamic permission data and standard permission data to the permission level monitoring unit;

[0078] It needs to be explained in detail that dynamic operation data refers to the dynamic data call volume and dynamic protection count of the mobile information block; dynamic permission data refers to the real-time mobile phone temperature data and unit time operation speed of the storage space where the mobile information block is located; standardized operation data refers to the standardized data call volume range and standardized protection count range of the mobile information block; and standardized permission data refers to the standardized mobile phone temperature data range and standardized operation speed range of the storage space where the mobile information block is located.

[0079] The mobile phone background monitoring unit is used to monitor the dynamic operation of mobile phone information blocks. The monitoring process is as follows:

[0080] like Figure 3 ​​As shown, the dynamic data call volume H of the mobile information block is obtained during different pop-up periods. M and dynamic protection times H X ;

[0081] Match the dynamic data call volume of different pop-up periods with the standard data call volume range, match the dynamic protection number of different pop-up periods with the standard protection number range, mark pop-up periods that are not in the standard data call volume range as non-standard call periods, and mark pop-up periods that are not in the standard protection number range as non-standard protection periods.

[0082] The number of non-routine call cycles and the number of non-routine protection cycles are respectively denoted as B. G and P Y ;

[0083] Through formula The runtime mutation value D of the mobile information block is calculated. Q In the formula, ξ and ψ are both mutation matrices composed of different factors;

[0084] The mobile phone background monitoring unit will detect the sudden change value D of the mobile phone information block. Q Send to the personal cloud data center, which will then send the mobile information block's runtime mutation value D. Q Send to the unauthorized data theft early warning unit;

[0085] The permission level monitoring unit is used to monitor the real-time permission status of the storage space where the mobile phone information block is located. The monitoring process is as follows:

[0086] like Figure 4 As shown, the real-time phone temperature data W of the storage space where the phone information block is located is obtained during different pop-up window cycles. T and the speed of operation per unit time V L ;

[0087] The real-time mobile phone temperature data of different pop-up periods is matched with the standard mobile phone temperature data range, the unit time running speed of different pop-up periods is matched with the standard running speed range, the pop-up periods that are not in the standard mobile phone temperature data range are marked as abnormal temperature periods, and the pop-up periods that are not in the standard running speed range are marked as abnormal running speed periods.

[0088] The number of abnormal temperature cycles and the number of abnormal operating speed cycles are respectively denoted as G. M and G Z ;

[0089] Through formula The permission mutation value J of the storage space where the mobile phone information block is located is calculated. QIn the formula, λ and η are both mutation matrices composed of different factors;

[0090] The permission level monitoring unit will monitor the permission mutation value J of the storage space where the mobile phone information block is located. Q Feedback is sent to the personal cloud data center, which then modifies the permission value J of the storage space where the mobile phone information block is located. Q Send to the unauthorized data theft early warning unit;

[0091] The personal cloud data center also sets a data access security warning value range for mobile information blocks based on the security warning pop-up window, and sends the data access security warning value range to the unauthorized data theft warning unit;

[0092] Among them, the data call security warning value range corresponding to the compliant APP security warning pop-up is (H N H D The data retrieval security warning value range corresponding to the dangerous APP security warning pop-up is (H) D N E The data retrieval security warning value range corresponding to the security warning pop-up for the non-compliant APP is (N). E N T ], H N H D N E and N T All are integers greater than zero, and H N >H D >N E >N T ;

[0093] The unauthorized data theft early warning unit is used to provide data retrieval security warnings based on the usage of mobile phone information blocks. The specific working process is as follows:

[0094] Obtain the runtime mutation value D of the mobile information block calculated above. Q The permission mutation value J of the storage space where the mobile phone information block is located. Q ;

[0095] Using formula The data access security warning value R of the mobile phone information block is calculated. C In the formula, α and β are mutation matrices composed of different factors;

[0096] A security alert pop-up window is generated to retrieve mobile phone information blocks. Based on the security alert pop-up window, the data retrieval security alert value range corresponding to the mobile phone information blocks is obtained.

[0097] When the data access security warning value is within the data access security warning value range corresponding to the mobile phone information block, a mobile phone data access warning signal is generated.

[0098] If the data call security warning value is not within the data call security warning value range corresponding to the mobile phone information block, a normal mobile phone operation signal is generated.

[0099] The unauthorized data theft warning unit feeds back the mobile phone data access warning signal or the mobile phone normal operation signal to the personal cloud data center. If the personal cloud data center receives the mobile phone normal operation signal, the mobile phone will operate normally. If the personal cloud data center receives the mobile phone data access warning signal, it will send the mobile phone data access warning signal to the warning APP corresponding to the mobile phone information block. After receiving the mobile phone data access warning signal, the warning APP will upgrade its permissions and restrict the permissions of other application APPs.

[0100] By using convolutional neural networks to build a deep learning model based on the results of each application's permission restrictions, the response speed of the warning app can be improved.

[0101] The deep learning model is established using the following expression:

[0102] E e =GE e-1 +H e ·α e

[0103] Among them, E e E represents the current permission scope of the application. e-1 This represents the permission scope of the application (APP) at the previous step, G represents the permission factor (a constant), He represents the factor affecting data retrieval changes, and α... e This indicates the data change value retrieved by the application at the current moment.

[0104] The above formulas are all dimensionless numerical calculations. The formulas are derived from software simulations using a large amount of integrated data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. The size of the mutation matrix and the proportion matrix is ​​to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. Regarding the size of the mutation matrix and the proportion matrix, it is acceptable as long as it does not affect the proportional relationship between the parameters and the quantized values.

[0105] like Figure 5 As shown, a data security early warning method based on neural networks includes:

[0106] A1, the permission classification unit categorizes mobile phone information into different types of information blocks for different purposes. Simultaneously, the database unit stores personal privacy data for these information blocks and sends this data to the existing data retrieval unit. The existing data retrieval unit monitors the data retrieval frequency of these information blocks. First, it obtains the data retrieval frequency A for each information block. Then, it obtains the specific date of each data retrieval and calculates the unit period for adjacent dates to obtain different data retrieval time intervals. The covariance of these time intervals is then calculated to obtain the data retrieval time fluctuation pattern B for the information blocks. Finally, it obtains the monitoring unit period for each data retrieval and performs a convolution operation on the data retrieval frequency within that monitoring unit period to obtain the monitoring security coefficient C for the information block. This is then used to calculate the security coefficient C for the information block. The existing data retrieval value D of the mobile information block is calculated. When D < W, the security warning pop-up of the mobile information block is a security warning pop-up of a non-compliant APP. When W ≤ D < S, the security warning pop-up of the mobile information block is a security warning pop-up of a dangerous APP. When S ≤ D, the security warning pop-up of the mobile information block is a security warning pop-up of a compliant APP. The existing data retrieval unit feeds back the security warning pop-up of the mobile information block to the personal cloud data center.

[0107] A2, the personal cloud data center sets the data access security warning period for the mobile information block, and sets different frequency pop-up cycles within the data access security warning period based on the security warning pop-up;

[0108] A3 integrates dynamic operation data of mobile information blocks for different purposes and dynamic permission data of the storage space where the mobile information blocks are located during the data call security warning period through the mobile information integration unit, and sends the dynamic operation data and dynamic permission data to the personal cloud data center. The personal cloud data center also stores the standardized operation data and standardized permission data of the mobile information blocks. The personal cloud data center sends the dynamic operation data and standardized operation data to the mobile background monitoring unit, and sends the dynamic permission data and standardized permission data to the permission level monitoring unit.

[0109] A4, monitor the dynamic operation of the mobile information block through the mobile background monitoring unit, and obtain the dynamic data call volume H of the mobile information block at different pop-up periods. M and dynamic protection times H XThe dynamic data call volume of different pop-up window cycles is matched with the standard data call volume range, and the dynamic protection count of different pop-up window cycles is matched with the standard protection count range. Pop-up window cycles that are not within the standard data call volume range are marked as non-standard call cycles, and pop-up window cycles that are not within the standard protection count range are marked as non-standard protection cycles. The number of non-standard call cycles and the number of non-standard protection cycles are counted and denoted as B. G and P Y Through formula The runtime mutation value D of the mobile information block is calculated. Q The mobile phone background monitoring unit will detect the sudden change value D of the mobile phone information block. Q Send to the personal cloud data center, which will then send the mobile information block's runtime mutation value D. Q Send to the unauthorized data theft early warning unit;

[0110] A5, the permission level monitoring unit monitors the real-time permission status of the storage space where the mobile phone information block is located, and obtains the real-time mobile phone temperature data W of the storage space where the mobile phone information block is located during different pop-up periods. T and the speed of operation per unit time V L The system matches real-time mobile phone temperature data from different pop-up window periods with the standard mobile phone temperature data range, and matches the unit-time running speed from different pop-up window periods with the standard running speed range. Pop-up window periods that do not fall within the standard mobile phone temperature data range are marked as abnormal temperature periods, and pop-up window periods that do not fall within the standard running speed range are marked as abnormal running speed periods. The number of abnormal temperature periods and the number of abnormal running speed periods are recorded as G. M and G Z Through formula The permission mutation value J of the storage space where the mobile phone information block is located is calculated. Q The permission level monitoring unit will monitor the permission mutation value J of the storage space where the mobile phone information block is located. Q Feedback is sent to the personal cloud data center, which then modifies the permission value J of the storage space where the mobile phone information block is located. Q Send to the unauthorized data theft early warning unit;

[0111] A6, the personal cloud data center also sets a data access security warning value range for mobile information blocks based on security warning pop-ups, and sends this range to the unauthorized data theft warning unit. The unauthorized data theft warning unit issues data access security warnings based on the usage of mobile information blocks and obtains the operational mutation value D of the mobile information blocks. Q The permission mutation value J of the storage space where the mobile phone information block is located. Q Using the formula The data access security warning value R of the mobile phone information block is calculated. C Then, the security warning pop-up window of the mobile phone information block is obtained. Based on the security warning pop-up window, the data call security warning value range corresponding to the mobile phone information block is obtained. When the data call security warning value is within the data call security warning value range corresponding to the mobile phone information block, a mobile phone data call warning signal is generated. When the data call security warning value is not within the data call security warning value range corresponding to the mobile phone information block, a normal mobile phone operation signal is generated.

[0112] A7, the unauthorized data theft warning unit feeds back the mobile phone data access warning signal or the mobile phone normal operation signal to the personal cloud data center. If the personal cloud data center receives the mobile phone normal operation signal, the mobile phone will operate normally. If the personal cloud data center receives the mobile phone data access warning signal, it will send the mobile phone data access warning signal to the warning APP corresponding to the mobile phone information block. After receiving the mobile phone data access warning signal, the warning APP will upgrade its permissions and restrict the permissions of other application APPs.

[0113] A8 uses a convolutional neural network to build a deep learning model based on the results of each application's permission restrictions, thereby improving the response speed of warning apps.

[0114] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A data security early warning system based on neural networks, characterized in that, The system includes a permission classification unit, an existing data retrieval unit, a mobile background monitoring unit, an unauthorized data theft early warning unit, a permission level monitoring unit, an early warning APP, a mobile information integration unit, and a personal cloud data center; The permission classification unit is used to classify mobile phone information by permission and divide it into mobile phone information blocks for different purposes; The existing data retrieval unit is connected to a database unit, which is used to store personal privacy data of mobile phone information blocks for different purposes and send them to the existing data retrieval unit. The existing data retrieval unit feeds back the security warning pop-up of the mobile phone information block to the personal cloud data center. The personal cloud data center is used to set the data retrieval security warning period of the mobile phone information block, and to set different frequency pop-up cycles within the data retrieval security warning period according to the security warning pop-up. The mobile information integration unit is used to integrate dynamic operation data of mobile information blocks for different purposes and dynamic permission data of the storage space where the mobile information blocks are located during the data call security warning period, and send them to the personal cloud data center; the personal cloud data center also stores the standardized operation data and standardized permission data of the mobile information blocks; the personal cloud data center sends the dynamic operation data and standardized operation data to the mobile background monitoring unit, and the personal cloud data center sends the dynamic permission data and standardized permission data to the permission level monitoring unit.

2. The data security early warning system based on a neural network according to claim 1, characterized in that, The existing data retrieval unit is used to monitor the data retrieval frequency of mobile phone information blocks for different purposes, including: The data call frequency of the mobile information block is obtained, and then the specific date of each data call of the mobile information block is obtained. The unit period of adjacent specific dates of data calls is calculated to obtain different data call time intervals. The covariance of different data call time intervals is calculated to obtain the data call time fluctuation pattern of the mobile information block. The monitoring unit period for each data call of the mobile phone information is obtained. The monitoring unit period for each data call is used to calculate the data call frequency to obtain the monitoring security coefficient of the mobile phone information block. The existing data retrieval value of the mobile phone information block is calculated. The existing data retrieval value is matched with the existing data retrieval range to determine whether the security warning pop-up of the mobile information block is a security warning pop-up of a non-compliant APP, a security warning pop-up of a dangerous APP, or a security warning pop-up of a compliant APP.

3. The data security early warning system based on neural networks according to claim 1, characterized in that, The mobile phone background monitoring unit is used to monitor the dynamic operation of the mobile phone information block, including: Get the dynamic data call volume and dynamic protection count of the mobile information block during different pop-up cycles; Match the dynamic data call volume of different pop-up periods with the standard data call volume range, match the dynamic protection number of different pop-up periods with the standard protection number range, mark pop-up periods that are not in the standard data call volume range as non-standard call periods, and mark pop-up periods that are not in the standard protection number range as non-standard protection periods. The number of unconventional call cycles and the number of non-conventional protection cycles are counted to calculate the runtime mutation value of the mobile information block.

4. A data security early warning system based on a neural network according to claim 1, characterized in that, The mobile phone background monitoring unit sends the runtime mutation value of the mobile phone information block to the personal cloud data center; the permission level monitoring unit is used to monitor the real-time permission status of the storage space where the mobile phone information block is located, including: Get real-time mobile phone temperature data and running speed per unit time of the storage space where the mobile phone information block is located during different pop-up periods; The real-time mobile phone temperature data of different pop-up periods is matched with the standard mobile phone temperature data range, the unit time running speed of different pop-up periods is matched with the standard running speed range, the pop-up periods that are not in the standard mobile phone temperature data range are marked as abnormal temperature periods, and the pop-up periods that are not in the standard running speed range are marked as abnormal running speed periods. Count the number of abnormal temperature cycles and the number of abnormal running speed cycles, and calculate the permission mutation value of the storage space where the mobile phone information block is located; The permission level monitoring unit feeds back the permission mutation value of the storage space where the mobile information block is located to the personal cloud data center. The personal cloud data center sends the operation mutation value of the mobile information block and the permission mutation value of the storage space where the mobile information block is located to the unauthorized data theft early warning unit.

5. A data security early warning system based on a neural network according to claim 1, characterized in that, The personal cloud data center also sets a data access security warning value range for mobile phone information blocks based on the security warning pop-up and sends it to the unauthorized data theft warning unit; the unauthorized data theft warning unit is used to issue data access security warnings based on the usage of mobile phone information blocks, including: Obtain the runtime mutation value of the mobile information block and the permission mutation value of the storage space where the mobile information block is located, and calculate the data access security warning value of the mobile information block; A security alert pop-up window is generated to retrieve mobile phone information blocks. Based on the security alert pop-up window, the data retrieval security alert value range corresponding to the mobile phone information blocks is obtained. When the data access security warning value is within the data access security warning value range corresponding to the mobile phone information block, a mobile phone data access warning signal is generated. If the data call security warning value is not within the data call security warning value range corresponding to the mobile phone information block, a normal mobile phone operation signal is generated. The unauthorized data theft early warning unit generates a mobile phone data access warning signal or a normal mobile phone operation signal and feeds it back to the personal cloud data center.

6. A data security early warning system based on a neural network according to claim 1, characterized in that, If the personal cloud data center receives a signal indicating that the phone is functioning normally, then the phone will operate normally. If the personal cloud data center receives a mobile data access warning signal, it will send the warning signal to the warning APP corresponding to the mobile information block. After receiving the mobile data access warning signal, the warning APP will upgrade its permissions and restrict the permissions of other application APPs.

7. A data security early warning system based on a neural network according to claim 7, characterized in that, By using convolutional neural networks to build a deep learning model based on the results of each application's permission restrictions, the response speed of the warning app can be improved. The deep learning model is established using the following expression: E e GE e-1 +H e α e Among them, E e E represents the current permission scope of the application. e-1 This represents the permission scope of the application (APP) at the previous step, G represents the permission factor (a constant), He represents the factor affecting data retrieval changes, and α... e This indicates the data change value retrieved by the application at the current moment.

8. A data security early warning method based on neural networks, characterized in that, The method includes: A1, the permission classification unit classifies mobile phone information into different permission categories, dividing it into mobile phone information blocks for different purposes. The existing data retrieval unit monitors the data retrieval frequency of mobile phone information blocks for different purposes and sends the security warning pop-up of the mobile phone information blocks to the personal cloud data center. A2, the personal cloud data center sets the data access security warning period for the mobile information block and sets different frequency pop-up cycles within the data access security warning period based on the security warning pop-up; A3, the mobile information integration unit integrates dynamic operation data of mobile information blocks for different purposes and dynamic permission data of the storage space where the mobile information blocks are located during the data call security warning period. The personal cloud data center also stores the standardized operation data and standardized permission data of the mobile information blocks. A4. The mobile phone background monitoring unit monitors the dynamic operation of the mobile phone information block and sends the abnormal operation value of the mobile phone information block to the unauthorized data theft early warning unit. A5 monitors the real-time permission status of the storage space where the mobile information block is located through the permission level monitoring unit, and sends the permission mutation value of the storage space where the mobile information block is located to the unauthorized data theft early warning unit. A6, Personal cloud data center combines security alert pop-up window to set data call security alert value range for mobile information block and send it to unauthorized data theft alert unit; A7, the unauthorized data theft warning unit provides data access security warnings on the use of mobile phone information blocks, and generates mobile phone data access warning signals or mobile phone normal operation signals. A8 uses a convolutional neural network to build a deep learning model based on the results of each application's permission restrictions, thereby improving the response speed of warning apps.

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