Data Dynamic Encryption Transmission Method and System Based on Remote Working Platform

Abstract

The present invention discloses a method and system for dynamically encrypting and transmitting data based on a remote office platform, which relates to the technical field of remote office and includes: obtaining all remote office terminals accessing the remote office platform; collecting sample work behaviors; extracting behavior feature data based on the sample work behaviors, denoted as sample work behavior feature data; obtaining a normal work behavior feature relationship database of the remote office terminals; obtaining the real-time work behaviors of the remote office terminals in real time; extracting several real-time work behavior features of the remote office terminals; calculating a reasonable index of the real-time work behaviors of the remote office terminals; determining the encryption level of data transmission of the remote office terminals; and generating encrypted data by using a data scrambling algorithm and transmitting it to the remote office terminals. The advantages of the present invention are as follows: analyzing and learning based on the behaviors of the remote office terminals during normal work of the remote office terminals, dynamically adjusting data encryption based on work behavior features, and ensuring the data security of the remote office platform.

Description

Technical Field

[0001] The present invention relates to the technical field of remote work, and specifically relates to a method and system for dynamically encrypting and transmitting data based on a remote work platform. Background Art

[0002] A remote work platform refers to a system or platform that allows employees to work collaboratively and communicate through the Internet and dedicated software tools when geographically dispersed. These platforms usually integrate multiple functions to support the efficient operation of remote teams, including but not limited to file sharing, real-time communication, project management, task assignment, video conferencing, schedule arrangement, online collaborative editing, etc.

[0003] The main purpose of remote work platforms is to break geographical restrictions, enabling team members to stay closely connected regardless of their location and achieve seamless collaboration. They provide remote workers with the necessary tools and environment to ensure the smooth progress of work processes while maintaining the security and confidentiality of information.

[0004] With the continuous development of technologies such as cloud computing, big data, and artificial intelligence, the functions and performance of remote work platforms are also constantly improving. Modern remote work platforms usually have high scalability, flexibility, and ease of use, and can meet the needs of enterprises of different scales and industries.

[0005] The secure transmission of data on remote work platforms is an important issue. Existing remote work platforms lack the analysis of the working behavior characteristics of remote work terminals. When a remote work terminal is hijacked, it is difficult to identify it in a timely manner, and it is unable to dynamically adjust the data encryption method, resulting in a risk of data leakage. Summary of the Invention

[0006] To solve the above technical problems, a method and system for dynamically encrypting and transmitting data based on a remote work platform are provided. This technical solution solves the problem that existing remote work platforms lack the analysis of the working behavior characteristics of remote work terminals. When a remote work terminal is hijacked, it is difficult to identify it in a timely manner, and it is unable to dynamically adjust the data encryption method, resulting in a risk of data leakage.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A method for dynamically encrypting and transmitting data based on a remote work platform, comprising:

[0009] Obtain all remote work terminals accessing the remote work platform;

[0010] Collect the historical working behaviors of the remote work terminals during normal work, and record them as sample working behaviors;

[0011] Extract behavioral feature data based on sample work behaviors, denoted as sample work behavior feature data, where the behavioral feature data at least includes access features and time features;

[0012] Analyze and learn based on the sample behavioral feature data to obtain a normal work behavior feature relationship database for the remote working terminal;

[0013] Obtain the behaviors during the working process of the remote working terminal in real time, denoted as the real-time work behaviors of the remote working terminal;

[0014] Extract several real-time work behavior features of the remote working terminal based on the real-time work behaviors of the remote working terminal;

[0015] Based on the real-time work behavior features of the remote working terminal, use a behavior risk identification algorithm to calculate the reasonable index of the real-time work behaviors of the remote working terminal;

[0016] Determine the encryption level of data transmission of the remote working terminal based on the reasonable index of the real-time work behaviors of the remote working terminal;

[0017] According to the encryption level of data transmission of the remote working terminal, use a data scrambling algorithm to generate encrypted data and transmit it to the remote working terminal.

[0018] Preferably, the analysis and learning based on the sample behavioral feature data to obtain a normal work behavior feature relationship database for the remote working terminal specifically includes:

[0019] Divide a day into several analysis time periods according to the set working duration;

[0020] Based on the time features in the sample work behaviors, determine the analysis time periods corresponding to the sample work behaviors;

[0021] Count all the sample work behaviors corresponding to each analysis time period;

[0022] Based on the access features of the sample work behaviors corresponding to each analysis time period, use a feature correlation algorithm to calculate the correlation degree between the access features corresponding to each analysis time period;

[0023] Encapsulate the sample work behaviors corresponding to each analysis time period and the correlation degree between the access features corresponding to each analysis time period into a normal work behavior feature relationship database for the remote working terminal.

[0024] Preferably, the feature correlation algorithm is specifically:

[0025] Denote the access features of the sample work behaviors corresponding to the analysis time period as the access features corresponding to the analysis time period;

[0026] Form an access feature set A for all the access features corresponding to the analysis time period, A = {a1,..., ai , …, a n , where a i is the i-th access feature corresponding to the analysis period, and n is the total number of access features corresponding to the analysis period;

[0027] Determine a number of sample work behaviors that occur for a i to form a first set of sample work behaviors;

[0028] Determine a number of sample work behaviors that occur for a j to form a second set of sample work behaviors;

[0029] Find the intersection of the first set of sample work behaviors and the second set of sample work behaviors to obtain a third set of sample work behaviors;

[0030] Find the union of the second set of sample work behaviors and the second set of sample work behaviors to obtain a fourth set of sample work behaviors;

[0031] Determine the number of elements in the first set of sample work behaviors, the number of elements in the second set of sample work behaviors, the number of elements in the third set of sample work behaviors, and the number of elements in the fourth set of sample work behaviors;

[0032] Calculate the correlation degree between a i and a j through the correlation calculation formula;

[0033] The specific correlation calculation formula is:

[0034] ;

[0035] In the formula, is the correlation degree between a i and a j , N0 is the total number of sample work behaviors corresponding to the analysis period, N1 is the number of elements in the first set of sample work behaviors, N2 is the number of elements in the second set of sample work behaviors, N 1∩2 is the number of elements in the third set of sample work behaviors, and N 1∪2 is the number of elements in the fourth set of sample work behaviors.

[0036] Preferably, the behavior risk identification algorithm specifically includes:

[0037] Based on the time feature in the real-time work behavior characteristics of the remote work terminal, determine the analysis period corresponding to the real-time work behavior of the remote work terminal, denoted as the real-time analysis period;

[0038] Retrieve the normal work behavior feature relationship database corresponding to the real-time analysis period;

[0039] Combine multiple real-time access characteristics of remote working terminals in the real-time working behavior characteristics of remote working terminals in pairs to form a number of real-time access characteristic groups of remote working terminals;

[0040] Based on two real-time access characteristics of remote working terminals in each real-time access characteristic group of remote working terminals, retrieve them in the normal working behavior characteristic relationship database, and determine the correlation between the two real-time access characteristics of remote working terminals in the real-time access characteristic group of remote working terminals as the rationality of the real-time access characteristic group of remote working terminals. If the correlation between the two real-time access characteristics of remote working terminals in each real-time access characteristic group of remote working terminals cannot be retrieved in the normal working behavior characteristic relationship database, assign a value of 0 to the rationality of this real-time access characteristic group of remote working terminals;

[0041] Calculate the behavioral rationality of the real-time working behavior of remote working terminals based on the rationality calculation formula;

[0042] The specific rationality calculation formula is:

[0043] ;

[0044] In the formula, H is the behavioral rationality of the real-time working behavior of remote working terminals, K is the total number of real-time access characteristic groups of remote working terminals, and X1 is the rationality of the l-th real-time access characteristic group of remote working terminals.

[0045] Preferably, determining the encryption level of remote working terminal data transmission based on the rationality index of the real-time working behavior of remote working terminals specifically includes:

[0046] Determine the maximum scrambling encryption level set by the remote working platform;

[0047] Based on the behavioral rationality of the real-time working behavior of remote working terminals and the maximum scrambling encryption level, determine the level index of remote working terminal data transmission through the level determination formula;

[0048] Based on the ceiling of the level index of remote working terminal data transmission, obtain the encryption level of remote working terminal data transmission;

[0049] Among them, the specific level determination formula is:

[0050] ;

[0051] In the formula, L’ is the level index of remote working terminal data transmission, and L max is the maximum scrambling encryption level.

[0052] Preferably, generating encrypted data according to the encryption level of remote office terminal data transmission and transmitting the encrypted data to the remote office terminal specifically includes:

[0053] Record the encryption level of remote office terminal data transmission as L;

[0054] Convert the remote office terminal data to be transmitted into binary data, and sequentially extract L 2 characters from the binary data in the order from front to back, denoted as the characteristic character segment. If the number of remaining characters at the end is less than L 2 , then add 0 bits to the end of the remaining characters at the end until the number of characters reaches L 2 ;

[0055] Determine the total number of the extracted characteristic character segments, denoted as M, and sequentially attach numbers between 1 - M to each characteristic character segment according to the front - back order of the characteristic character segments in the binary data;

[0056] Randomly sort the integers between 1 - M to obtain the initial scrambling key;

[0057] Sequentially sort the corresponding characteristic character segments according to the sorting of the integers between 1 - M in the initial scrambling key to obtain the initial scrambled data;

[0058] Adopt the position scrambling algorithm to perform position scrambling encryption on each characteristic character segment in the initial scrambled data to obtain the encrypted data.

[0059] Preferably, the position scrambling algorithm is specifically:

[0060] Generate an L×L grid, and randomly fill the positive integers from 1 - L 2 into the L×L grid;

[0061] According to the character sequence numbers in the characteristic character segment, fill the characters in the characteristic character segment into the positions of the positive integers from 1 - L 2 in the L×L grid;

[0062] Extract the characters from the L×L grid in the order from left to right and from top to bottom to obtain the position - encrypted character segment of the characteristic character segment.

[0063] Furthermore, a data dynamic encryption transmission system based on a remote office platform is proposed, which is used to implement the data dynamic encryption transmission method based on the remote office platform as described above, including:

[0064] A log module, which is used to obtain all remote office terminals accessing the remote office platform and collect the historical work behaviors of the remote office terminals during normal work, denoted as sample work behaviors;

[0065] A behavior analysis module, which is electrically connected to the log module. The behavior analysis module is used to extract behavior feature data based on the sample work behavior, denoted as sample work behavior feature data. The behavior feature data at least includes access features and time features, and analyzes and learns based on the sample behavior feature data to obtain a normal work behavior feature relationship database of the remote office terminal;

[0066] A work behavior monitoring module, which is electrically connected to the behavior analysis module. The work behavior monitoring unit is used to obtain the behavior during the work process of the remote office terminal in real time, denoted as the real-time work behavior of the remote office terminal, and extract several real-time work behavior features of the remote office terminal based on the real-time work behavior of the remote office terminal. Based on the real-time work behavior features of the remote office terminal, a behavior risk identification algorithm is used to calculate the reasonable index of the real-time work behavior of the remote office terminal;

[0067] An encryption module, which is electrically connected to the work behavior monitoring module. The encryption module is used to determine the encryption level of the data transmission of the remote office terminal based on the reasonable index of the real-time work behavior of the remote office terminal, and generate encrypted data according to the encryption level of the data transmission of the remote office terminal by using a data scrambling algorithm and transmit it to the remote office terminal.

[0068] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0069] Since the remote office terminal corresponds to the office worker one by one and the office habits of the office workers are consistent, based on this, this solution analyzes and learns the behavior of the remote office terminal during normal work of the remote office terminal to obtain a normal work behavior feature relationship database that conforms to the behavior habits of the remote office terminal during work. Based on the normal work behavior feature relationship database, behavior risk identification is carried out on the real-time work behavior of the remote office terminal during the work process of the remote office terminal. When the behavior that does not conform to the behavior habits of the remote office terminal during work is identified, it is determined that the risk of the remote office terminal being hijacked is high. Therefore, a high-level encryption method is used for data transmission. In this way, even if the data is hijacked and leaked, due to the high difficulty of decrypting the high-level encrypted data, it can effectively resist brute-force cracking attacks and ensure the data security of the remote office platform. Brief Description of the Drawings

[0070] Figure 1 It is a flow chart of the data dynamic encryption transmission method based on the remote office platform proposed by this solution;

[0071] Figure 2 It is a flow chart of the method for obtaining the normal work behavior feature relationship database of the remote office terminal in this solution;

[0072] Figure 3 It is the method flow chart of the behavior risk identification algorithm in this solution;

[0073] Figure 4 It is the method flow chart for determining the encryption level of data transmission of remote office terminals in this solution;

[0074] Figure 5 It is the method flow chart for generating encrypted data using the data scrambling algorithm and transmitting it to remote office terminals in this solution;

[0075] Figure 6 It is the method flow chart of the location scrambling algorithm in this solution;

[0076] Figure 7 It is the architecture diagram of the electronic device in this solution;

[0077] Figure 8 It is the schematic diagram of the structure of the computer-readable storage medium in this solution. Specific implementation manners

[0078] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0079] Refer to Figure 1 As shown, a data dynamic encryption transmission method based on a remote office platform includes:

[0080] Obtain all remote office terminals accessing the remote office platform;

[0081] Collect the historical work behaviors of the remote office terminals during normal work, and record them as sample work behaviors;

[0082] Extract behavior feature data based on the sample work behaviors, and record them as sample work behavior feature data. The behavior feature data includes at least access features and time features;

[0083] Analyze and learn based on the sample behavior feature data to obtain a normal work behavior feature relationship database of the remote office terminals;

[0084] Obtain the behaviors of the remote office terminals during the work process in real time, and record them as the real-time work behaviors of the remote office terminals;

[0085] Extract several real-time work behavior features of the remote office terminals based on the real-time work behaviors of the remote office terminals;

[0086] Based on the real-time work behavior features of the remote office terminals, use the behavior risk identification algorithm to calculate the reasonable indicators of the real-time work behaviors of the remote office terminals;

[0087] Determine the encryption level of data transmission of the remote working terminal based on reasonable metrics of the real-time working behavior of the remote working terminal;

[0088] According to the encryption level of data transmission of the remote working terminal, use a data scrambling algorithm to generate encrypted data and transmit it to the remote working terminal.

[0089] Analyze and learn based on the behavior of the remote working terminal during normal work, obtain a normal working behavior feature relationship database that conforms to the behavior habits of the remote working terminal during work. Based on the normal working behavior feature relationship database, identify the behavior risks of the real-time working behavior of the remote working terminal during the work process. When identifying behaviors that do not conform to the behavior habits of the remote working terminal during work, it is determined that the risk of the remote working terminal being hijacked is high, so a high-level encryption method is used for data transmission.

[0090] Refer to Figure 2 As shown, the analysis and learning based on the sample behavior feature data to obtain the normal working behavior feature relationship database of the remote working terminal specifically includes:

[0091] Divide a day into several analysis time periods according to the set working hours;

[0092] Based on the time feature in the sample work behavior, determine the analysis time period corresponding to the sample work behavior;

[0093] Count all the sample work behaviors corresponding to each analysis time period;

[0094] Based on the access features of the sample work behaviors corresponding to each analysis time period, use the feature correlation algorithm to calculate the correlation degree between the access features corresponding to each analysis time period;

[0095] Encapsulate the correlation degree between the sample work behaviors corresponding to each analysis time period and the access features corresponding to each analysis time period into the normal working behavior feature relationship database of the remote working terminal.

[0096] The feature correlation algorithm is specifically:

[0097] Record the access features of the sample work behaviors corresponding to the analysis time period as the access features corresponding to the analysis time period;

[0098] Form an access feature set A for all the access features corresponding to the analysis time period, A = {a1,..., a i ,..., a n}, where a i is the i-th access feature corresponding to the analysis time period, and n is the total number of access features corresponding to the analysis time period;

[0099] Determine several sample work behaviors that occur within the analysis period and form a first set of sample work behaviors; i ;

[0100] Determine several sample work behaviors that occur within the analysis period and form a second set of sample work behaviors; j ;

[0101] Find the intersection of the first set of sample work behaviors and the second set of sample work behaviors to obtain a third set of sample work behaviors;

[0102] Find the union of the second set of sample work behaviors and the second set of sample work behaviors to obtain a fourth set of sample work behaviors;

[0103] Determine the number of elements in the first set of sample work behaviors, the number of elements in the second set of sample work behaviors, the number of elements in the third set of sample work behaviors, and the number of elements in the fourth set of sample work behaviors;

[0104] Calculate the correlation degree between i and j through a correlation calculation formula;

[0105] The specific correlation calculation formula is:

[0106] ;

[0107] is the correlation degree between i and j , N0 is the total number of sample work behaviors corresponding to the analysis period, N1 is the number of elements in the first set of sample work behaviors, N2 is the number of elements in the second set of sample work behaviors, N 1∩2 is the number of elements in the third set of sample work behaviors, and N 1∪2 is the number of elements in the fourth set of sample work behaviors.

[0108] The behaviors of workers during normal work are usually periodic every day. This periodic behavior habit is determined by the daily routine of the workers. In some preferred embodiments, the daily routine is further classified into holidays and working days according to the attributes of the date, and the learning is analyzed based on holidays and working days respectively to obtain a normal work behavior characteristic relationship database for holidays and a normal work behavior characteristic relationship database for working days;

[0109] In this solution, the correlation calculation formula consists of two parts. One part is the proportion of the usage behaviors corresponding to i and j in the usage behaviors in the current period, that is, , which represents the behavior habit of the worker. The other part isi and a j The rationality of their simultaneous occurrence in the same row, that is , this part represents whether the behavior is normal, and is calculated by combining two indicators: the user's behavior habits and whether the behavior is normal i and a j The correlation between them, the larger the value of the correlation indicates that i and a j The higher the rationality of their simultaneous occurrence during the current analysis period.

[0110] Refer to Figure 3 shown, the behavior risk identification algorithm specifically includes:

[0111] Based on the time feature in the real-time working behavior characteristics of the remote working terminal, determine the analysis period corresponding to the real-time working behavior of the remote working terminal, denoted as the real-time analysis period;

[0112] Retrieve the normal working behavior feature relationship database corresponding to the real-time analysis period;

[0113] Combine multiple real-time access characteristics of the remote working terminal in the real-time working behavior characteristics of the remote working terminal in pairs to form several real-time access characteristic groups of the remote working terminal;

[0114] Based on two real-time access characteristics of each real-time access characteristic group of the remote working terminal, retrieve in the normal working behavior feature relationship database to determine the correlation between the two real-time access characteristics of the real-time access characteristic group of the remote working terminal, as the rationality of the real-time access characteristic group of the remote working terminal. If the correlation between the two real-time access characteristics of each real-time access characteristic group of the remote working terminal cannot be retrieved in the normal working behavior feature relationship database, then assign a value of 0 to the rationality of the real-time access characteristic group of the remote working terminal;

[0115] Based on the rationality calculation formula, calculate the behavior rationality of the real-time working behavior of the remote working terminal;

[0116] The rationality calculation formula is specifically:

[0117] ;

[0118] In the formula, H is the behavior rationality of the real-time working behavior of the remote working terminal, K is the total number of real-time access characteristic groups of the remote working terminal, and X1 is the rationality of the lth real-time access characteristic group of the remote working terminal.

[0119] Refer to Figure 4 shown, based on the rationality index of the real-time working behavior of the remote working terminal, determining the encryption level of the data transmission of the remote working terminal specifically includes:

[0120] Determine the maximum scrambling encryption level set by the remote working platform;

[0121] Based on the behavioral rationality of the real-time working behavior of the remote working terminal and the maximum scrambling encryption level, determine the level index of the data transmission of the remote working terminal through the level determination formula;

[0122] Round up based on the level index of the data transmission of the remote working terminal to obtain the encryption level of the data transmission of the remote working terminal;

[0123] Among them, the level determination formula is specifically:

[0124] ;

[0125] In the formula, L’ is the level index of the data transmission of the remote working terminal, and L max is the maximum scrambling encryption level.

[0126] Determine the encryption level based on the reasonable level. The larger the reasonable index, the more in line with the working habits of the worker the remote working terminal is, and the lower the risk of being hijacked. Therefore, the encryption level can be appropriately reduced.

[0127] Refer to Figure 5 As shown, generating encrypted data according to the encryption level of the data transmission of the remote working terminal and transmitting it to the remote working terminal by using the data scrambling algorithm specifically includes:

[0128] Record the encryption level of the data transmission of the remote working terminal as L;

[0129] Convert the data of the remote working terminal to be transmitted into binary data, and sequentially extract L 2 characters from the binary data in the order from front to back, and record them as the characteristic character segment. If the number of remaining characters at the end is less than L 2 , then add 0 bits at the end of the remaining characters at the end until the number of characters reaches L 2 ;

[0130] Determine the total number of the extracted characteristic character segments, record it as M, and sequentially attach numbers between 1 and M to each characteristic character segment according to the front-back order of the characteristic character segments in the binary data;

[0131] Randomly sort the integers between 1 and M to obtain the initial scrambling key;

[0132] Sort the characteristic character segments correspondingly in sequence according to the sorting of the integers between 1 and M in the initial scrambling key to obtain the initial scrambled data;

[0133] Adopt the position scrambling algorithm to perform position scrambling encryption on each characteristic character segment in the initial scrambled data to obtain the encrypted data.

[0134] Referring to Figure 6 as shown, the square position scrambling algorithm is specifically as follows:

[0135] Generate an L×L grid and randomly fill the positive integers from 1 to L 2 into the L×L grid;

[0136] Number them in the order of the characters in the feature character segment, and fill the characters in the feature character segment into the positions of the positive integers from 1 to L 2 in the L×L grid;

[0137] Extract the characters from the L×L grid in the order from left to right and from top to bottom to obtain the orientation encrypted character segment of the feature character segment.

[0138] Adopt a double scrambling encryption method to dynamically adjust the encryption level. Since the initial scrambling method is used to obtain the initial scrambled data, and the smaller the encryption level, the lower the encryption level of the square position scrambling algorithm, but the encryption level of the initial scrambling method designed in this scheme will be higher, thus effectively ensuring the comprehensive encryption security of the data and ensuring the data security at a low encryption level.

[0139] Furthermore, based on the same inventive concept as the above-mentioned data dynamic encryption transmission method based on a remote office platform, this scheme proposes a data dynamic encryption transmission system based on a remote office platform, including:

[0140] A log module, which is used to obtain all remote office terminals accessing the remote office platform and collect the historical work behaviors of the remote office terminals during normal work, recorded as sample work behaviors;

[0141] A behavior analysis module, which is electrically connected to the log module. The behavior analysis module is used to extract behavior feature data based on the sample work behaviors, recorded as sample work behavior feature data. The behavior feature data at least includes access features and time features, and analyze and learn based on the sample behavior feature data to obtain a normal work behavior feature relationship database of the remote office terminals;

[0142] A work behavior monitoring module, which is electrically connected to the behavior analysis module. The work behavior monitoring unit is used to obtain the behaviors of the remote office terminals during the work process in real time, recorded as the real-time work behaviors of the remote office terminals, and extract several real-time work behavior features of the remote office terminals based on the real-time work behaviors of the remote office terminals. Based on the real-time work behavior features of the remote office terminals, calculate the reasonable index of the real-time work behaviors of the remote office terminals by using a behavior risk identification algorithm;

[0143] An encryption module, which is electrically connected to the work behavior monitoring module. The encryption module is used to determine the encryption level of the data transmission of the remote office terminal based on the reasonable metrics of the real-time work behavior of the remote office terminal, and generate encrypted data using a data scrambling algorithm according to the encryption level of the data transmission of the remote office terminal, and transmit the encrypted data to the remote office terminal.

[0144] Furthermore, the method according to the embodiment of the present application can also be implemented by means of Figure 7 the architecture of the electronic device shown. As Figure 7 shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to the network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, can store a method for dynamically encrypting data transmission based on a remote office platform provided by the present application. The electronic device 500 may also include a user interface 508. Of course, Figure 7 the architecture shown is only exemplary. When implementing different devices, one or more components shown in the Figure 7 electronic device may be omitted according to actual needs.

[0145] Figure 8 is a schematic diagram of the structure of a computer-readable storage medium provided by an embodiment of the present application. As Figure 8 shown, it is a computer-readable storage medium 600 according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are run by a processor, a method for dynamically encrypting data transmission based on a remote office platform according to the embodiment of the present application described with reference to the above drawings can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0146] In summary, the advantages of the present invention are as follows: Analyze and learn based on the behavior of the remote office terminal during normal work, and dynamically adjust data encryption based on work behavior characteristics to ensure the data security of the remote office platform.

[0147] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, various changes and improvements will occur to the present invention, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for dynamically encrypting and transmitting data based on a telecommuting platform, characterized in that Including: Obtain all remote working terminals accessing the remote working platform; Collect the historical working behaviors of the remote working terminals during normal working processes, denoted as sample working behaviors; Extract behavior feature data based on the sample working behaviors, denoted as sample working behavior feature data, where the behavior feature data at least includes access features and time features; Conduct analysis and learning based on the sample behavior feature data to obtain a normal working behavior feature relationship database of the remote working terminals; Obtain the behaviors of the remote working terminals during the working process in real time, denoted as the real-time working behaviors of the remote working terminals; Extract several real-time working behavior features of the remote working terminals based on the real-time working behaviors of the remote working terminals; Based on the real-time working behavior features of the remote working terminals, use a behavior risk identification algorithm to calculate the reasonable index of the real-time working behaviors of the remote working terminals; Determine the encryption level of the data transmission of the remote working terminals based on the reasonable index of the real-time working behaviors of the remote working terminals; According to the encryption level of the data transmission of the remote working terminals, use a data scrambling algorithm to generate encrypted data and transmit it to the remote working terminals; The determining the encryption level of the data transmission of the remote working terminals based on the reasonable index of the real-time working behaviors of the remote working terminals specifically includes: Determine the maximum level of scrambling encryption set by the remote working platform; Based on the behavior rationality of the real-time working behaviors of the remote working terminals and the maximum level of scrambling encryption, determine the level index of the data transmission of the remote working terminals through a level determination formula; Round up based on the level index of the data transmission of the remote working terminals to obtain the encryption level of the data transmission of the remote working terminals; Wherein, the specific level determination formula is: ； In the formula, is the level index of the data transmission of the telecommuting terminal, is the maximum scrambling encryption level, is the behavioral rationality of the real-time work behavior of the telecommuting terminal; The specifically including that according to the encryption level of the data transmission of the remote working terminals, using a data scrambling algorithm to generate encrypted data and transmit it to the remote working terminals specifically includes: Denote the encryption level of the data transmission of the remote working terminals as L; Convert the remote office terminal data to be transmitted into binary data, and extract characters in sequence from the binary data from front to back, which is recorded as the characteristic character segment. If the number of remaining characters at the end is less than , add 0 bits at the end of the remaining characters until the number of characters reaches ; characters, and denote it as the characteristic character segment. If the number of remaining characters at the end is less than , then add 0 bits at the end of the remaining characters until the number of characters reaches ; Determine the total number of extracted feature character segments, denoted as M, and sequentially assign numbers between 1 and M to each feature character segment according to the front-back order of the feature character segments in the binary data; Randomly sort the integers between 1 and M to obtain an initial scrambling key; According to the sorting of the integers between 1 and M in the initial scrambling key, sequentially sort the corresponding feature character segments to obtain initial scrambled data; Use a position scrambling algorithm to perform position scrambling encryption on each feature character segment in the initial scrambled data to obtain encrypted data; The specific position scrambling algorithm is: Generate an \(L\times L\) grid and randomly fill the positive integers from 1 to into the \(L\times L\) grid; Number according to the character order in the feature character segment, 1 - Fill the characters in the feature character segment into the positions of positive integers in the L×L grid; Extract characters from the L×L grid in sequence from left to right and top to bottom to obtain the position encryption character segment of the feature character segment.

2. The data dynamic encryption transmission method based on a remote working platform according to claim 1, wherein The specifically including that conducting analysis and learning based on the sample behavior feature data to obtain a normal working behavior feature relationship database of the remote working terminals specifically includes: Divide a day into several analysis time periods according to the set working duration; Based on the time features in the sample working behaviors, determine the analysis time periods corresponding to the sample working behaviors; Count all the sample working behaviors corresponding to each analysis time period; Based on the access features of the sample working behaviors corresponding to each analysis time period, use a feature correlation algorithm to calculate the correlation degree between the access features corresponding to each analysis time period; Encapsulate the correlation between the sample work behaviors corresponding to each analysis period and the access characteristics corresponding to each analysis period into the normal work behavior feature relationship database of the remote office terminal.

3. A data dynamic encryption transmission method based on a remote work platform according to claim 2, characterized in that The specific feature correlation algorithm is as follows: Denote the access characteristics of the sample work behaviors corresponding to the analysis period as the access characteristics corresponding to the analysis period; All access features corresponding to the analysis period are grouped into an access feature set A, , where is the i-th access feature corresponding to the analysis period, is the total number of access features corresponding to the analysis period; Determine a number of sample work behaviors that occur during the analysis period to form a first set of sample work behaviors; Determine a number of sample work behaviors that occur during the analysis period to form a second set of sample work behaviors; Find the intersection of the first sample work behavior set and the second sample work behavior set to obtain the third sample work behavior set; Find the union of the second sample work behavior set and the second sample work behavior set to obtain the fourth sample work behavior set; Determine the number of elements in the first sample work behavior set, the number of elements in the second sample work behavior set, the number of elements in the third sample work behavior set, and the number of elements in the fourth sample work behavior set; Calculate the correlation between and ; The specific correlation calculation formula is as follows: ； In the formula, is the correlation between and is the total number of sample work behaviors corresponding to the analysis period, is the number of elements in the first set of sample work behaviors, is the number of elements in the second set of sample work behaviors, is the number of elements in the third set of sample work behaviors, is the number of elements in the fourth set of sample work behaviors.

4. A data dynamic encryption transmission method based on a remote office platform according to claim 3, characterized in that The specific behavior risk identification algorithm includes: Based on the time feature in the real-time work behavior characteristics of the remote office terminal, determine the analysis period corresponding to the real-time work behavior of the remote office terminal, denoted as the real-time analysis period; Retrieve the normal work behavior feature relationship database corresponding to the real-time analysis period; Combine multiple real-time access characteristics of the remote office terminal in the real-time work behavior characteristics of the remote office terminal pairwise into several real-time access characteristic groups of the remote office terminal; Based on the two real-time access characteristics of the remote office terminal in each real-time access characteristic group of the remote office terminal, retrieve them in the normal work behavior feature relationship database, and determine the correlation between the two real-time access characteristics of the remote office terminal in the real-time access characteristic group of the remote office terminal as the rationality of the real-time access characteristic group of the remote office terminal. If the correlation between the two real-time access characteristics of the remote office terminal in each real-time access characteristic group of the remote office terminal cannot be retrieved in the normal work behavior feature relationship database, then assign a value of 0 to the rationality of the real-time access characteristic group of the remote office terminal; Based on the rationality calculation formula, calculate the behavior rationality of the real-time work behavior of the remote office terminal; The specific rationality calculation formula is as follows: ； In the formula, is the behavior rationality of the real-time working behavior of the telecommuting terminal, is the total number of real-time access feature groups of the telecommuting terminal, is the rationality of the l-th real-time access feature group of the telecommuting terminal.

5. A data dynamic encryption and transmission system based on a remote working platform, characterized in that, To implement the data dynamic encryption transmission method based on the remote office platform as described in any one of claims 1-4, including: A log module, which is used to obtain all remote office terminals accessing the remote office platform and collect the historical work behaviors of the remote office terminals during normal work, denoted as sample work behaviors; A behavior analysis module, which is electrically connected to the log module. The behavior analysis module is used to extract behavior feature data based on the sample work behaviors, denoted as sample work behavior feature data. The behavior feature data at least includes access characteristics and time characteristics, and analyze and learn based on the sample behavior feature data to obtain the normal work behavior feature relationship database of the remote office terminal; A work behavior monitoring module, which is electrically connected to the behavior analysis module. The work behavior monitoring unit is used to obtain the behaviors during the work process of the remote office terminal in real time, denoted as the real-time work behaviors of the remote office terminal, and extract several real-time work behavior characteristics of the remote office terminal based on the real-time work behaviors of the remote office terminal. Based on the real-time work behavior characteristics of the remote office terminal, a behavior risk identification algorithm is used to calculate the reasonable index of the real-time work behaviors of the remote office terminal. An encryption module, which is electrically connected to the work behavior monitoring module. The encryption module is used to determine the encryption level of the data transmission of the remote office terminal based on the reasonable index of the real-time work behaviors of the remote office terminal, and generate encrypted data according to the encryption level of the data transmission of the remote office terminal by using a data scrambling algorithm and transmit it to the remote office terminal.

6. An electronic device, characterized in that, Comprising: At least one processor; And a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for dynamically encrypting and transmitting data based on a remote office platform as described in any one of claims 1-4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a method for dynamically encrypting and transmitting data based on a remote office platform as described in any one of claims 1-4.

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