Calculation network security analysis method based on artificial intelligence

Abstract

The invention discloses a computing network security analysis method based on artificial intelligence, relates to the technical field of computing network security, and solves the technical problems that in the prior art, natural factor analysis cannot be performed on hardware during computing network security supervision, and whether the current network computing processing performance meets requirements cannot be deduced. The method specifically comprises the following steps: natural factor analysis: performing natural factor analysis on computer hardware during operation of a computing network, and inferring whether the current network computing processing performance meets requirements or not according to the computer hardware analysis; architecture security detection: carrying out architecture security detection on the computing network; system configuration detection: carrying out system configuration detection on the computing network, and detecting the system security in the computing network when the operation duration of the computing network is continuously increased; and program security detection: carrying out security detection on the covering operation program in the operation process of the computing network.

Description

Technical Field

[0001] The present invention relates to the field of computing network security technology, and in particular to a computing network security analysis method based on artificial intelligence. Background Art

[0002] Computer network security refers to the protection of hardware, software and data resources in computer network systems to prevent them from being destroyed, altered or leaked due to accidental or malicious reasons, ensuring the continuous, reliable and normal operation of the system and uninterrupted network services.

[0003] However, in the existing technology, it is impossible to analyze the natural factors of hardware during computer network security supervision, it is impossible to infer whether the current network computing processing performance meets the requirements, and it is impossible to perform security detection on the network architecture, so that it is impossible to ensure whether the system adapts to the current data processing requirements. In addition, it is impossible to monitor the network system configuration and cover the security monitoring of running programs, which reduces the security performance of the computer network.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and to propose a computational network security analysis method based on artificial intelligence.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A computational network security analysis method based on artificial intelligence, the computational network security analysis method steps are as follows:

[0008] Natural factor analysis: natural factor analysis of computer hardware is performed when the computing network is running, and the current network computing processing performance is inferred based on the computer hardware analysis to see whether it meets the requirements;

[0009] Architecture security testing: Perform architecture security testing on the computing network to ensure that the computing network system processing can adapt to current data processing needs while being able to conduct network equipment security assessments;

[0010] System configuration detection: perform system configuration detection on the computing network and detect the security of the system in the computing network when the running time of the computing network continues to increase;

[0011] Program security detection, which performs security detection on running programs during the operation of the computing network. By running programs, the data processing diversity of the computing network can be improved.

[0012] As a preferred embodiment of the present invention, the natural factor analysis process is as follows:

[0013] Computer hardware performance detection collects natural factor information and substitutes it into the formula to obtain the hardware performance analysis coefficient. The performance analysis coefficient at each moment in the network operation period is statistically calculated, and each performance analysis coefficient is connected according to the time sequence to construct a performance display curve; and the performance display curve is subjected to fluctuation analysis; the peak value of the performance display curve within the operation period is obtained. When the fluctuation of the peak value exceeds the set numerical threshold or the peak value fluctuation frequency, the current period is marked as a performance consumption period. On the contrary, when the fluctuation of the peak value does not exceed the set numerical threshold and the peak value fluctuation frequency, the current period is marked as a performance peak period.

[0014] As a preferred embodiment of the present invention, numerical parameters and trend parameters are collected. If the numerical parameters exceed the speed numerical ratio threshold, or the trend parameters exceed the slope span threshold, the computer hardware performance is determined to be abnormal when in the performance consumption period; and the computer hardware performance is determined to be decreasing when in the performance peak period.

[0015] If the numerical parameter does not exceed the speed numerical ratio threshold, and the trend parameter does not exceed the slope span threshold, then in the performance consumption period, it is determined that the computer hardware performance is in a constant trend under the current processing task; in the performance peak period, it is determined that the computer hardware performance is in an upward trend.

[0016] As a preferred embodiment of the present invention, the natural factor information includes the corresponding numerical ratio of the peak increase span of the excess amount of the environmental parameters of the computer hardware exceeding the set threshold during the operation of the computing network to the floating frequency increase speed of the environmental parameters exceeding the threshold, the sum of the corresponding cumulative increase speeds of the increase speed of the computer hardware surface wear during the operation of the computing network and the corresponding function execution delay increase speed of the computer hardware, and the ratio of the sum of the corresponding times of hardware port failures and the number of incompatibility and unrecognition between hardware during the data transmission of the computer hardware to the number of transmission interruptions.

[0017] As a preferred embodiment of the present invention, the numerical parameters and trend parameters are respectively the corresponding speed numerical ratio of the peak update growth span increase rate of the performance display curve to the peak update interval duration increase rate, and the reciprocating floating span value of the floating slope of the performance display curve within the current peak update interval period.

[0018] As a preferred implementation of the present invention, the architecture security detection process is as follows:

[0019] The architecture information is collected and the architecture security assessment coefficient is obtained based on the information processing. If the architecture security assessment coefficient exceeds the architecture security assessment coefficient threshold, it is inferred that the architecture security detection of the computing network is abnormal; if the architecture security assessment coefficient does not exceed the architecture security assessment coefficient threshold, it is inferred that the architecture security detection of the computing network is normal.

[0020] As a preferred embodiment of the present invention, the architecture information includes the delay of network connection interruption calculated by the remaining hardware devices when the data transmission of the central node is interrupted, the total accumulated delay time of the current network data transmission calculated after the backup central node is switched within the delay of the network connection interruption calculated by the hardware device, and the numerical ratio of the delay time for updating the firmware version of the hardware device in the calculation network to the total non-data processing time of the hardware device in the corresponding delay period.

[0021] As a preferred implementation of the present invention, the system configuration detection process is as follows:

[0022] The operation configuration information and server configuration information are obtained. If the operation configuration information exceeds the data rate value ratio threshold, or the server configuration information does not exceed the mean duration deviation threshold, it is inferred that the system configuration detection in the computing network is abnormal; if the operation configuration information does not exceed the data rate value ratio threshold, and the server configuration information exceeds the mean duration deviation threshold, it is inferred that the system configuration detection in the computing network is normal.

[0023] As a preferred embodiment of the present invention, the operation configuration information and the server configuration information are respectively the numerical ratio of the proportion of low-security password accounts in the operating system of the computing network and the numerical ratio of the increase rate of the security reminder frequency of the corresponding accounts when the operating system reminds them of low-security passwords for the first time, and the maximum mean duration deviation after adjustment of the storage duration of the storage data with an increasing usage frequency trend and the storage data with a non-increasing usage frequency trend in the server configuration of the computing network.

[0024] As a preferred implementation of the present invention, the specific process of program security detection is as follows:

[0025] During the operation of the computing network, the application code security check is performed. If there is no filtering operation of the account private information in the execution code corresponding to the functional operation of the application in the computing network, or the amount of account private information filtered data in the execution code does not exceed the set data volume threshold, it is inferred that the functional operation of the current application is risky;

[0026] If the functional operation of an application program in the computing network corresponds to a filtering operation of the account private information in the execution code, and the amount of filtered data of the account private information in the execution code exceeds the set data amount threshold, it is inferred that there is no risk in the functional operation of the current application program.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. In the present invention, natural factors analysis is performed on computer hardware when the computing network is running, and whether the current network computing processing performance meets the requirements is inferred based on the computer hardware analysis. By performing real-time status evaluation on the computing network during operation based on real-time performance detection, the computer network status can be controlled in real time, and the trend can be analyzed to timely regulate the computer to ensure that the data processing of the computing network is more stable;

[0029] Conduct architectural security testing on the computing network to ensure that the computing network system processing can adapt to current data processing needs while being able to conduct network equipment security assessments. Through network equipment assessments, the security of data processing is ensured, and the impact of network vulnerabilities during data processing is reduced, thereby reducing the operating efficiency of the computing network.

[0030] 2. In the present invention, the system configuration of the computing network is detected, and the system security in the computing network is detected when the running time of the computing network continues to increase, and it is inferred whether the current system configuration can efficiently and safely support the data processing of the computing network to ensure the feasibility of data processing. At the same time, ensuring system security can further ensure the comprehensiveness of the operation of the computing network, minimize the impact of data processing caused by the hidden dangers of the computing network itself, and improve the operation efficiency of the computing network;

[0031] During the operation of the computing network, security checks are performed on the running programs. The running programs can improve the data processing diversity of the computing network. Therefore, security checks on the running programs can directly control the data processing efficiency of the computing network, ensure the security and executability of data processing, avoid external intrusions that affect the execution security of the running programs, and increase the security risks of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0033] Figure 1 A flow chart of the overall method of the present invention;

[0034] Figure 2 This is a flow chart of the method for natural factor analysis in the present invention;

[0035] Figure 3 This is a flow chart of the method for architecture security detection in the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0038] See also Figure 1 As shown, a computational network security analysis method based on artificial intelligence is shown, and the specific steps of the computational network security analysis method are as follows:

[0039] Natural factor analysis: natural factor analysis of computer hardware is performed when the computing network is running. Based on the computer hardware analysis, it is inferred whether the current network computing processing performance meets the requirements. Through real-time performance detection, the real-time status of the computing network can be evaluated when it is running, which can ensure real-time control of the computer network status, facilitate trend analysis, and timely adjust the computer to ensure more stable data processing of the computing network;

[0040] Architecture security testing: Perform architecture security testing on the computing network to ensure that the computing network system processing can adapt to the current data processing needs while being able to conduct network equipment security assessments. Through network equipment assessments, the security of data processing is ensured, and the impact of network vulnerabilities during data processing is reduced, thereby reducing the operating efficiency of the computing network.

[0041] System configuration detection: perform system configuration detection on the computing network. When the computing network operation time continues to increase, detect the system security in the computing network to infer whether the current system configuration can efficiently and safely support the data processing of the computing network to ensure the feasibility of data processing. At the same time, ensuring system security can further ensure the comprehensive operation of the computing network, minimize the impact of data processing caused by the hidden dangers of the computing network itself, and improve the operation efficiency of the computing network;

[0042] Program security detection: security detection of running programs during the operation of the computing network. The running programs can improve the data processing diversity of the computing network. Therefore, security detection of running programs can directly control the data processing efficiency of the computing network, ensure the security and executability of data processing, avoid external intrusions affecting the execution security of running programs, and increase the security risks of data processing.

[0043] See also Figure 2 As shown, the natural factor analysis process is as follows:

[0044] Computer hardware performance detection, collect the numerical ratio of the peak value increase span of the excess output of the environmental parameters of the computer hardware exceeding the set threshold value during the operation of the computing network to the corresponding numerical ratio of the floating frequency increase speed of the environmental parameters exceeding the threshold value, where the collected numerical ratio is used to analyze the impact of numerical fluctuations of the two types of data, without considering the inconsistency of the units of the two types of data, and mark the numerical ratio of the peak value increase span of the excess output of the environmental parameters of the computer hardware exceeding the set threshold value during the operation of the computing network to the corresponding numerical ratio of the floating frequency increase speed of the environmental parameters exceeding the threshold value as KSB; environmental parameters are expressed as parameters such as humidity, ground water volume, and acid gas concentration in the area where the computer hardware equipment is located;

[0045] The sum of the increasing speed of the surface wear of the computer hardware and the increasing speed of the corresponding function execution delay of the computer hardware during the operation of the computing network is obtained, wherein the sum of the increasing speed only collects the sum of the values ​​of the two types of data, without considering the different units of the two speeds; and the sum of the increasing speed of the surface wear of the computer hardware and the increasing speed of the corresponding function execution delay of the computer hardware during the operation of the computing network is marked as SZH;

[0046] Obtain the ratio of the sum of the number of hardware port failures and the number of hardware incompatibility and unrecognition times to the number of transmission interruptions during the computer hardware data transmission process, and mark the ratio of the sum of the number of hardware port failures and the number of hardware incompatibility and unrecognition times to the number of transmission interruptions during the computer hardware data transmission process as CSB;

[0047] The above collected data are uniformly marked as natural factor information, and substituted into the formula to obtain the computer hardware performance analysis coefficient in the computing network. The formula is: Wherein, XN is the hardware performance analysis coefficient, hjk1, hjk2 and hjk3 are preset proportional coefficients, and α is an error correction factor, which takes a value of 0.97 when the computer hardware combination is within the warranty period, and takes a value of 1.03 when the computer hardware combination is not within the warranty period;

[0048] The performance analysis coefficients of the computer hardware at each moment in the computing network operation period are counted, and each performance analysis coefficient is connected according to the time sequence to construct a performance display curve; and the performance display curve is subjected to fluctuation analysis;

[0049] Get the peak value of the performance display curve during the running period. When the fluctuation of the peak value exceeds the set numerical threshold or the floating frequency of the peak value, the current period is marked as the performance consumption period. On the contrary, when the fluctuation of the peak value does not exceed the set numerical threshold and the floating frequency of the peak value, the current period is marked as the peak performance period.

[0050] The speed value ratio of the peak update growth span increase speed to the peak update interval time increase speed of the performance display curve is obtained, and the floating slope reciprocating floating span value of the performance display curve in the current peak update interval period is obtained. The speed value ratio of the peak update growth span increase speed to the peak update interval time increase speed of the performance display curve and the floating slope reciprocating floating span value of the performance display curve in the current peak update interval period are marked as numerical parameters and trend parameters, respectively, and compared with the speed value ratio threshold and the slope span threshold, respectively:

[0051] If the speed value ratio of the peak update growth span increase speed of the performance display curve to the peak update interval time increase speed exceeds the speed value ratio threshold, or the reciprocating floating span value of the floating slope of the performance display curve in the current peak update interval period exceeds the slope span threshold, then when it is in the performance consumption period, it is determined that the computer hardware performance is abnormal, and a maintenance signal is generated and sent to the administrator; when it is in the peak performance period, it is determined that the computer hardware performance is on a downward trend, and a maintenance signal is generated and sent to the administrator;

[0052] If the speed value ratio of the peak update growth span increase speed of the performance display curve to the peak update interval time increase speed does not exceed the speed value ratio threshold, and the reciprocating floating span value of the floating slope of the performance display curve in the current peak update interval period does not exceed the slope span threshold, then when it is in the performance consumption period, it is determined that the computer hardware performance is showing a constant trend under the current processing task, and a continuous monitoring signal is generated and sent to the administrator; when it is in the peak performance period, it is determined that the computer hardware performance is showing an upward trend, and a normal signal is generated and sent to the administrator;

[0053] See also Figure 3 As shown, the architecture security detection process is as follows:

[0054] Analyze the network topology of the current computing network, take the switch as the central node, obtain the delay of the computing network connection interruption of the remaining hardware devices when the data transmission of the central node is interrupted, and collect the total cumulative delay time of the current computing network data transmission after the backup central node is switched within the delay of the computing network connection interruption of the hardware device, and mark the total cumulative delay time of the computing network data transmission of the remaining hardware devices when the data transmission of the central node is interrupted and the total cumulative delay time of the current computing network data transmission after the backup central node is switched within the delay of the computing network connection interruption of the hardware device as ZD and YL respectively;

[0055] Obtain the numerical ratio of the firmware version update delay time of the hardware device in the computing network to the total non-data processing time of the hardware device in the corresponding delay period, wherein the update delay time is represented by the interval time between the time when the firmware version reaches the set version and the time when the current firmware version completes the highest version upgrade, and mark the numerical ratio of the firmware version update delay time of the hardware device in the computing network to the total non-data processing time of the hardware device in the corresponding delay period as SB;

[0056] The above collected information is uniformly marked as architecture information, and substituted into the formula to obtain the architecture security assessment coefficient of the computing network, where the formula is: Where JA represents the architecture safety assessment coefficient, h1, h2 and h3 are preset proportional coefficients, and β is the error correction factor, which is 0.98;

[0057] Compare the architecture security assessment coefficient JA with the architecture security assessment coefficient threshold:

[0058] If the architecture security assessment coefficient JA exceeds the architecture security assessment coefficient threshold, it is inferred that the architecture security detection of the computing network is abnormal, and an architecture rectification signal is generated and sent to the administrator. After receiving it, the administrator will rectify the architecture of the current computing network, that is, to give advance warning of failures to the hardware devices corresponding to the central node and to supervise the execution efficiency of the backup devices, so as to avoid the decrease in the computing network data processing efficiency due to the smooth completion of the network architecture;

[0059] If the architecture security assessment coefficient JA does not exceed the architecture security assessment coefficient threshold, it is inferred that the architecture security detection of the computing network is normal, and an architecture qualified signal is generated and sent to the administrator;

[0060] The system configuration detection process is as follows:

[0061] The numerical ratio of the proportion of low-security password accounts in the operating system of the computing network to the speed of increase in the frequency of security reminders for the accounts corresponding to the first low-security password reminders of the operating system is obtained, where the low-security password account is represented by a user whose current account has a single password type or has not changed the corresponding type for a long time. At the same time, the maximum average duration deviation of the storage data with an increasing usage frequency trend and the storage data with a non-increasing usage trend after the storage duration adjustment in the server configuration of the computing network is obtained, and the numerical ratio of the proportion of low-security password accounts in the operating system of the computing network to the speed of increase in the frequency of security reminders for the accounts corresponding to the first low-security password reminders of the operating system, and the maximum average duration deviation of the storage data with an increasing usage frequency trend and the storage data with a non-increasing usage trend in the server configuration of the computing network after the storage duration adjustment are marked as operation configuration information and server configuration information, respectively, and compared with the data rate numerical ratio threshold and the mean duration deviation threshold, respectively:

[0062] If the ratio of the number of low-security password accounts in the operating system of the computing network to the speed of increase in the frequency of security reminders for the accounts corresponding to the first low-security password reminders of the operating system exceeds the numerical ratio threshold, or the maximum mean duration deviation after adjustment of the storage duration of the storage data with an increasing usage frequency trend and the storage data with a non-increasing usage frequency trend in the server configuration of the computing network does not exceed the mean duration deviation threshold, then it is inferred that the system configuration detection in the computing network is abnormal, and a system configuration risk signal is generated and sent to the administrator. After receiving the signal, the administrator increases the operating system security detection performance of the computing network and sets security settings for the account, and manages the storage data of the server configuration by category, increases the monitoring of the data storage duration, and controls the storage of different types of durations;

[0063] If the ratio of the number of low-security password accounts in the operating system of the computing network to the corresponding ratio of the frequency increase speed of the security reminder of the corresponding account of the low-security password for the first time in the operating system does not exceed the numerical ratio threshold, and the maximum mean duration deviation of the storage data with an increasing usage frequency trend and the storage data with a non-increasing usage frequency trend after adjustment in the server configuration of the computing network exceeds the mean duration deviation threshold, it is inferred that the system configuration detection in the computing network is normal, and a system configuration security signal is generated and sent to the administrator;

[0064] The specific process of program security detection is as follows:

[0065] During the operation of the computing network, the application code security check is performed. If there is no filtering operation for the account private information in the execution code corresponding to the application function operation in the computing network, or the account private information filtering data volume in the execution code does not exceed the set data volume threshold, it is inferred that the current application function operation has risks, and the private information of the executing and pending account is modified, and the code of the current function operation of the application is re-edited and a filtering plug-in is added; the private information is represented by the account user information, account number password, etc.

[0066] If the application function operation in the computing network corresponds to the account private information in the execution code, and the account private information filtering data volume in the execution code exceeds the set data volume threshold, it is inferred that there is no risk in the function operation of the current application, and all applications in the computing network are checked one by one until they are completely checked; and after the detection, a program security signal is generated and sent to the administrator;

[0067] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technicians in this field according to actual conditions;

[0068] When the present invention is in use, natural factor analysis is performed on computer hardware when the computing network is running, and whether the current network computing processing performance meets the requirements is inferred based on the computer hardware analysis; architecture security detection is performed on the computing network to ensure that the computing network system processing adapts to the current data processing requirements while being able to perform network equipment security assessment; system configuration detection is performed on the computing network to detect the system security within the computing network when the computing network operation time continues to increase; program security detection is performed on the running programs covered during the operation of the computing network, and the data processing diversity of the computing network can be improved by running the programs.

[0069] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A computational network security analysis method based on artificial intelligence, characterized in that: The steps of computational network security analysis method are as follows: Natural factor analysis: natural factor analysis of computer hardware is performed when the computing network is running, and the current network computing processing performance is inferred based on the computer hardware analysis to see whether it meets the requirements; Architecture security testing: Perform architecture security testing on the computing network to ensure that the computing network system processing can adapt to current data processing needs while being able to conduct network equipment security assessments; System configuration detection: perform system configuration detection on the computing network and detect the security of the system in the computing network when the running time of the computing network continues to increase; Program security detection, which performs security detection on running programs during the operation of the computing network. By running programs, the data processing diversity of the computing network can be improved.

2. The method for computing network security analysis based on artificial intelligence according to claim 1, characterized in that: The natural factor analysis process is as follows: Computer hardware performance detection, collects natural factor information, and substitutes it into the formula to obtain the hardware performance analysis coefficient, calculates the performance analysis coefficient at each moment in the network operation period, and connects each performance analysis coefficient according to the time sequence to construct a performance display curve; and performs fluctuation analysis on the performance display curve; The peak value of the performance display curve during the running period is obtained. When the fluctuation of the peak value exceeds the set numerical threshold or the floating frequency of the peak value, the current period is marked as the performance consumption period. On the contrary, when the fluctuation of the peak value does not exceed the set numerical threshold and the floating frequency of the peak value, the current period is marked as the peak performance period.

3. The method for computing network security analysis based on artificial intelligence according to claim 2, characterized in that: The numerical parameters and trend parameters are collected. If the numerical parameters exceed the speed numerical ratio threshold, or the trend parameters exceed the slope span threshold, it is determined that the computer hardware performance is abnormal when it is in the performance consumption period; When the performance is at its peak, it is determined that the computer hardware performance is declining; If the numerical parameter does not exceed the speed numerical ratio threshold, and the trend parameter does not exceed the slope span threshold, then when in the performance consumption period, it is determined that the computer hardware performance shows a constant trend under the current processing task; when in the performance peak period, it is determined that the computer hardware performance shows an upward trend.

4. The method for computing network security analysis based on artificial intelligence according to claim 3 is characterized in that: The natural factor information includes the numerical ratio of the peak increase span of the excess amount of environmental parameters in the computer hardware exceeding the set threshold during the operation of the computing network to the numerical ratio of the increase rate of the floating frequency of the environmental parameters exceeding the threshold, the sum of the increase rate of the computer hardware surface wear during the operation of the computing network and the cumulative increase rate of the corresponding function execution delay increase rate of the computer hardware, and the ratio of the sum of the corresponding times of hardware port failures and the times of incompatibility and unrecognition between hardware during the data transmission of computer hardware to the number of transmission interruptions.

5. The method for computing network security analysis based on artificial intelligence according to claim 4, characterized in that: The numerical parameters and trend parameters are respectively the speed numerical ratio of the peak update growth span increase speed of the performance display curve to the peak update interval time increase speed, and the reciprocating floating span value of the floating slope of the performance display curve within the current peak update interval period.

6. The method for computing network security analysis based on artificial intelligence according to claim 1, characterized in that: The architecture security testing process is as follows: Collecting architecture information and obtaining an architecture security assessment coefficient based on information processing. If the architecture security assessment coefficient exceeds an architecture security assessment coefficient threshold, it is inferred that the architecture security detection of the computing network is abnormal. If the architecture security assessment coefficient does not exceed the architecture security assessment coefficient threshold, it is inferred that the architecture security detection of the computing network is normal.

7. The method for computing network security analysis based on artificial intelligence according to claim 6, characterized in that: The architecture information includes the delay in calculating the network connection interruption of the remaining hardware devices when the data transmission of the central node is interrupted, the total cumulative delay in the current computing network data transmission after the backup central node is switched during the hardware device computing network connection interruption delay, and the numerical ratio of the delay in updating the firmware version of the hardware device in the computing network to the total non-data processing time of the hardware device in the corresponding delay period.

8. The method for computing network security analysis based on artificial intelligence according to claim 1, characterized in that: The system configuration detection process is as follows: The operation configuration information and server configuration information are obtained. If the operation configuration information exceeds the data rate value ratio threshold, or the server configuration information does not exceed the mean duration deviation threshold, it is inferred that the system configuration detection in the computing network is abnormal; if the operation configuration information does not exceed the data rate value ratio threshold, and the server configuration information exceeds the mean duration deviation threshold, it is inferred that the system configuration detection in the computing network is normal.

9. The method for computing network security analysis based on artificial intelligence according to claim 1, characterized in that: The operation configuration information and server configuration information respectively include the ratio of the number of low-security password accounts in the operating system of the computing network to the numerical ratio of the increase rate of security reminders for the accounts corresponding to the first low-security password reminders of the operating system, and the maximum mean duration deviation after adjustment of the storage duration of the storage data with an increasing usage frequency trend and the storage data with a non-increasing usage frequency trend in the server configuration of the computing network.

10. The method for computing network security analysis based on artificial intelligence according to claim 1, characterized in that: The specific process of program security detection is as follows: During the operation of the computing network, the application code security check is performed. If there is no filtering operation of the account private information in the execution code corresponding to the functional operation of the application in the computing network, or the amount of account private information filtered data in the execution code does not exceed the set data volume threshold, it is inferred that the functional operation of the current application is risky; If the functional operation of an application program in the computing network corresponds to a filtering operation of the account private information in the execution code, and the amount of filtered data of the account private information in the execution code exceeds the set data amount threshold, it is inferred that there is no risk in the functional operation of the current application program.

Images