Network security data analysis method based on artificial intelligence

Abstract

The invention belongs to the technical field of network security, discloses a network security data analysis method based on artificial intelligence, and provides an analysis method for network data security during cross-platform data collaboration. The method comprises the following steps: firstly, extracting characteristic parameters of identity authentication, data encryption and access transmission in a cross-platform data cooperation process, carrying out dimensionless processing on the characteristic parameters, unifying different parameters to the same scale, and calculating and acquiring a corresponding safety coefficient based on the characteristic parameters of identity authentication, data encryption and access transmission safety; according to the method, the security coefficient in the cross-platform data cooperation process at the present stage is evaluated based on different security coefficients, the security of the cross-platform data cooperation at the next stage is predicted according to the evaluation result at the present stage, the network data security risk is accurately identified through multi-dimensional data analysis, and the accuracy and comprehensiveness of security detection are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of network security, and particularly relates to a network security data analysis method based on artificial intelligence. Background Art

[0002] With the rapid development of information technology, network security issues have become increasingly severe. Endless network attack means pose a huge threat to the information security of individuals, enterprises, and even countries. Traditional network security protection methods mainly analyze network security data based on the comparison of network characteristic parameters and thresholds.

[0003] However, with the rapid development of information technology, cross-platform data collaboration has become increasingly common in organizations such as enterprises and research institutions. Data interaction and sharing between different platforms have greatly improved work efficiency and resource utilization rate. However, due to differences in the architectures, security policies, and data formats of different platforms, data is vulnerable to attacks during transmission and storage. Existing network security protection means often cannot comprehensively and accurately address the complex security issues during cross-platform data collaboration. Therefore, developing an analysis method for network security data during cross-platform data collaboration has important practical significance. Summary of the Invention

[0004] The purpose of the present invention is to provide a network security data analysis method based on artificial intelligence to solve the problems faced in the above background art.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A network security data analysis method based on artificial intelligence, the method includes the following steps: Step S1, collect cross-platform data collaboration data packets through a network traffic collection device; Step S2, extract the characteristic parameters in the data packets, and process all the characteristic parameters based on artificial intelligence to unify different parameters to the same scale; Step S3, analyze the characteristic parameters, and evaluate the security of cross-platform data collaboration at the current stage according to the analysis results; Step S4, predict the security of cross-platform data collaboration in the next stage according to the analysis results.

[0006] As a further description of the technical solution of the present invention, the specific working process of step S2 includes: The characteristic parameters include: identity authentication parameters, data encryption parameters, and access transmission parameters; The identity authentication parameters include: password complexity parameters and multi-factor authentication parameters; The data encryption parameters include: encryption algorithm parameters and key management parameters; The access transmission parameters include: transmission protocol parameters and data integrity verification parameters.

[0007] As a further description of the technical solution of the present invention, the specific working process of step S3 includes: Step S31: Obtain the authentication, data encryption, and access transmission security coefficients respectively; Step S32: Obtain the cross-platform data collaboration security coefficient based on the authentication, data encryption, and access transmission security coefficients; Step S33: Compare the authentication, data encryption, and access transmission security coefficients with the system-set authentication, data encryption, and access transmission security coefficient thresholds respectively. If any security coefficient is greater than or equal to the system-set security coefficient threshold, it means that the cross-platform data collaboration security is unqualified. If no security coefficient is greater than or equal to the system-set security coefficient threshold, go to step S34; Step S34: Compare the cross-platform data collaboration security coefficient with the system-set cross-platform data collaboration security coefficient threshold. If the cross-platform data collaboration security coefficient is greater than or equal to the system-set cross-platform data collaboration security coefficient threshold, it means that there are potential risks in the cross-platform data collaboration security.

[0008] As a further description of the technical solution of the present invention, the working process of obtaining the authentication security coefficient in step S31 includes: Obtain the authentication parameters, and construct an authentication security coefficient calculation model based on the authentication parameters. The expression is: ; In the formula, is the authentication security coefficient, is the password complexity index coefficient, is the verification code index coefficient, and are the weight coefficients corresponding to the password complexity index coefficient and the verification code index coefficient respectively.

[0009] As a further description of the technical solution of the present invention, the working process of obtaining the data encryption security coefficient in step S31 includes: Obtain the data encryption parameters, and construct a data encryption security coefficient calculation model based on the data encryption parameters. The expression is: ; In the formula, is the data encryption security coefficient, is the encryption algorithm index coefficient, is the key management index coefficient, and They are the weight coefficients corresponding to the encryption algorithm index coefficient and the key management index coefficient respectively.

[0010] As a further description of the technical solution of the present invention, the working process of obtaining the access transmission security coefficient in step S31 includes: Obtain the access transmission parameters, and construct an access transmission security coefficient calculation model based on the access transmission parameters. The expression is: ; In the formula, is the access transmission security coefficient, is the transmission protocol index coefficient, is the data integrity verification index coefficient, and and are the weight coefficients corresponding to the transmission protocol index coefficient and the data integrity verification index coefficient respectively.

[0011] As a further description of the technical solution of the present invention, the method for obtaining the cross-platform data collaboration security coefficient in step S32 includes: Construct a cross-platform data collaboration security coefficient calculation model. The expression is: ; In the formula, , and are the weight coefficients corresponding to the identity authentication security coefficient, the data encryption security coefficient and the transmission security coefficient respectively.

[0012] As a further description of the technical solution of the present invention, the working process of step S4 includes: Obtain the current stage identity authentication parameters, data encryption parameters and access transmission parameters, substitute them into the cross-platform data collaboration security coefficient calculation model, and obtain the current stage cross-platform data collaboration security coefficient , construct a cross-platform data collaboration security coefficient calculation model for the next stage. The expression is: ; In the formula, After normalization processing, the value range is from 0 to 1, is the cross-platform data collaboration security coefficient for the next stage, and the value range is from 0 to 1, is the influence of sudden factors. When there are no sudden factors, W = 0. When there are sudden factors and positive effects, W = 1. When there are sudden factors and negative effects, W = -1, is the self-regulation coefficient, and the stronger the self-regulation ability the larger; Input the cross-platform data collaboration security coefficient for the next stage into the trained artificial intelligence model to predict the cross-platform data collaboration security level for the next stage of the next stage.

[0013] Advantages of the present invention: 1. The present invention provides an analysis method for network data security during cross-platform data collaboration. First, acquisition authentication, data encryption, and access transmission characteristic parameters during the cross-platform data collaboration process are extracted, and the characteristic parameters are made dimensionless to unify different parameters to the same scale. Based on the security characteristic parameters of authentication, data encryption, and access transmission, corresponding security coefficients are calculated. Based on different security coefficients, the security system during the current stage of cross-platform data collaboration is evaluated, and the security of the next stage of cross-platform data collaboration is predicted according to the current evaluation results. Through multi-dimensional data analysis, network data security risks are accurately identified, and the accuracy and comprehensiveness of security detection are improved; 2. Based on the risk assessment results during the current stage of cross-platform data collaboration and combined with environmental factors, the risks during the next stage of cross-platform data collaboration are predicted, which can quickly adapt to the constantly changing cross-platform attack patterns and timely discover new security threats.

[0014] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0016] Figure 1 It is a partial process schematic diagram of the network security data analysis method based on artificial intelligence of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Please refer to Figure 1 As shown, a network security data analysis method based on artificial intelligence is disclosed, and the method includes the following steps: Step S1: Collect cross-platform data collaboration data packets through a network traffic collection device; Step S2: Extract the characteristic parameters in the data packet, and process all the characteristic parameters based on artificial intelligence to unify different parameters to the same scale; Step S3: Analyze the characteristic parameters, and evaluate the security of cross-platform data collaboration in the current stage according to the analysis results; Step S4: Predict the security of cross-platform data collaboration in the next stage according to the analysis results.

[0019] Through the above technical solution, the present invention provides an analysis method for network data security during cross-platform data collaboration. First, extract the identity authentication, data encryption, and access transmission characteristic parameters in the cross-platform data collaboration process, perform dimensionless processing on the characteristic parameters to unify different parameters to the same scale, calculate the corresponding security coefficients based on the identity authentication, data encryption, and access transmission security characteristic parameters, evaluate the security system in the current stage of cross-platform data collaboration based on different security coefficients, and predict the security of cross-platform data collaboration in the next stage according to the evaluation results of the current stage. Through multi-dimensional data analysis, accurately identify network data security risks, and improve the accuracy and comprehensiveness of security detection.

[0020] As a further description of the technical solution of the present invention, the specific working process of step S2 includes: The characteristic parameters include: identity authentication parameters, data encryption parameters, and access transmission parameters; The identity authentication parameters include: password complexity parameters and multi-factor authentication parameters; The data encryption parameters include: encryption algorithm parameters and key management parameters; The access transmission parameters include: transmission protocol parameters and data integrity verification parameters.

[0021] As a further description of the technical solution of the present invention, the specific working process of step S3 includes: Step S31: Obtain the identity authentication, data encryption, and access transmission security coefficients respectively; Step S32: Obtain the cross-platform data collaboration security coefficient based on the identity authentication, data encryption, and access transmission security coefficients; Step S33: Compare the identity authentication, data encryption, and access transmission security coefficients with the system-set identity authentication, data encryption, and access transmission security coefficient thresholds respectively. If any security coefficient is greater than or equal to the system-set security coefficient threshold, it means that the security of cross-platform data collaboration is unqualified. If no security coefficient is greater than or equal to the system-set security coefficient threshold, go to step S34; Step S34: Compare the cross-platform data collaboration security coefficient with the cross-platform data collaboration security coefficient threshold set by the system. If the cross-platform data collaboration security coefficient is greater than or equal to the cross-platform data collaboration security coefficient threshold set by the system, it indicates that there are potential risks in the cross-platform data collaboration security.

[0022] As a further description of the technical solution of the present invention, the working process of obtaining the authentication security coefficient in step S31 includes: Obtain authentication parameters, and construct an authentication security coefficient calculation model based on the authentication parameters. The expression is: ; In the formula, is the authentication security coefficient, is the password complexity index coefficient, is the verification code index coefficient, and are the weight coefficients corresponding to the password complexity index coefficient and the verification code index coefficient respectively; The method for obtaining the password complexity index coefficient includes: Obtain the password length , obtain the minimum password length requirement and the maximum expected length ; Obtain the password character type , obtain the minimum password character type requirement and the maximum expected character type ; Construct a password complexity index coefficient calculation model. The expression is: ; The method for obtaining the verification code index coefficient includes: Obtain the verification code error retry times R , obtain the error retry times interval set by the system; Obtain the verification code verification duration T, and obtain the verification code verification duration interval set by the system; Construct a verification code index coefficient calculation model. The expression is: .

[0023] As a further description of the technical solution of the present invention, the working process of obtaining the data encryption security coefficient in step S31 includes: Obtain data encryption parameters, and construct a data encryption security coefficient calculation model based on the data encryption parameters. The expression is: ; Wherein, is the data encryption security coefficient, is the encryption algorithm index coefficient, is the key management index coefficient, and are the weight coefficients corresponding to the encryption algorithm index coefficient and the key management index coefficient respectively; The method for obtaining the encryption algorithm index coefficient includes: Obtain the key length of K bits, and assign values to the encryption algorithm index coefficient according to the key length. When k = 128, When k = 192, When k = 256, ; The method for obtaining the key management index coefficient includes: Obtain the random number seed length of and obtain the random number seed length interval set by the system ; Obtain the key distribution validity period of and obtain the key distribution validity period interval set by the system ; Construct a key management index coefficient calculation model, and the expression is: .

[0024] As a further description of the technical solution of the present invention, the working process of obtaining the access transmission security coefficient in step S31 includes: Obtain the access transmission parameters, and construct an access transmission security coefficient calculation model based on the access transmission parameters. The expression is: ; Wherein, is the access transmission security coefficient, is the transmission protocol index coefficient, is the data integrity verification index coefficient, and and are the weight coefficients corresponding to the transmission protocol index coefficient and the data integrity verification index coefficient respectively; The method for obtaining the transmission protocol index coefficient includes: Obtain the protocol certificate validity period of and obtain the key distribution validity period interval set by the system ; Assign values to the protocol trust level according to the actual investigation results. When the trust rate exceeds 80%, the trust level When the trust rate does not exceed 80%, the trust level ; Construct a calculation model for the transmission protocol metric coefficient, and the expression is: ; The method for obtaining the data integrity verification metric coefficient includes: Obtain the hash value comparison fault tolerance parameter as , and obtain the range of the hash value comparison fault tolerance parameter set by the system ; ; In the formula, is the hash value algorithm metric coefficient, which is assigned according to the hash value algorithm used in the transmission protocol.

[0025] Through the above technical solution, this embodiment provides a risk assessment for cross-platform data collaboration. First, obtain the password length , obtain the minimum password length requirement set by the system and the maximum expected length , the password character type , obtain the minimum password character type requirement set by the system and the maximum expected character type , calculate the password complexity metric coefficient according to the obtained parameters, obtain the number of times of incorrect verification code retry R , obtain the range of the number of incorrect retry times set by the system , obtain the verification code verification duration T, obtain the range of the verification code verification duration set by the system , calculate the verification code metric coefficient according to the obtained parameters, and perform weighted summation on the password complexity metric coefficient and the verification code metric coefficient to obtain the identity verification security coefficient; then, obtain the key length, assign a value to the encryption algorithm metric coefficient based on the obtained key length, obtain the random number seed length as , obtain the range of the random number seed length set by the system , obtain the key distribution validity period as , obtain the range of the key distribution validity period set by the system , calculate the key management metric coefficient according to the obtained parameters, and perform weighted summation on the encryption algorithm metric coefficient and the key management metric coefficient to obtain the encryption security coefficient; then, assign a value to the protocol trust level according to the actual investigation results, obtain the protocol certificate validity period as , obtain the range of the key distribution validity period set by the system , calculate the transmission protocol metric coefficient according to the assignment result and the obtained parameters, obtain the hash value comparison fault tolerance parameter as , and obtain the range of the hash value comparison fault tolerance parameter set by the system , assign values to the hash value algorithm index coefficients according to the hash value algorithm used by the transmission protocol, calculate the data integrity verification index coefficients based on the obtained parameters and the assignment results, and perform weighted summation on the transmission protocol index coefficients and the data integrity verification index coefficients to obtain the access transmission security coefficient.

[0026] As a further description of the technical solution of the present invention, the method for obtaining the cross-platform data collaboration security coefficient in step S32 includes: Construct a cross-platform data collaboration security coefficient calculation model, and the expression is: ;

[0027] In the formula, , and are the weight coefficients corresponding to the authentication security coefficient, the data encryption security coefficient, and the transmission security coefficient respectively.

[0028] As a further description of the technical solution of the present invention, the working process of step S4 includes: Obtain the current stage authentication parameters, data encryption parameters, and access transmission parameters, substitute them into the cross-platform data collaboration security coefficient calculation model, and obtain the current stage cross-platform data collaboration security coefficient , construct a cross-platform data collaboration security coefficient calculation model for the next stage, and the expression is: ; In the formula, After normalization processing, the value range is from 0 to 1, is the cross-platform data collaboration security coefficient for the next stage, and the value range is from 0 to 1, is the influence of sudden factors. When there are no sudden factors, W = 0. When there are sudden factors and positive effects, W = 1. When there are sudden factors and negative effects, W = -1, is the self-regulation coefficient, and the stronger the self-regulation ability the larger; The self-regulation ability refers to the ability of the platform to ensure the data security system by adjusting specific parameters in cross-platform collaboration, such as security questions, video authentication, etc.

[0029] Input the cross-platform data collaboration security coefficient for the next stage into the trained artificial intelligence model to predict the cross-platform data collaboration security level for the next stage of the next stage.

[0030] Through the above technical solution, this embodiment provides a method for obtaining the security coefficient of cross-platform data collaboration in the current stage and the next stage. According to the parameters in the current stage, the identity authentication security coefficient and the data encryption security coefficient in the current stage are calculated respectively. Then, based on the identity authentication security coefficient and the data encryption security coefficient, the cross-platform data collaboration security coefficient in the current stage is calculated. Finally, in combination with the self-regulating ability and sudden environmental factors, the cross-platform data collaboration security coefficient in the next stage is calculated.

[0031] It should be noted that all parameters in the present invention have been dimensionless processed, and all calculations are pure numerical calculations. The parameters, thresholds, and threshold intervals involved in the present invention are all empirical data and will not be elaborated.

[0032] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the specific embodiments described or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by this claims, they should all belong to the protection scope of the present invention.

Claims

1. A method for network security data analysis based on artificial intelligence, characterized in that, The method includes the following steps: Step S1: Collect cross-platform data collaboration data packets through a network traffic collection device; Step S2: Extract the characteristic parameters in the data packets, and process all the characteristic parameters based on artificial intelligence to unify different parameters to the same scale; Step S3: Analyze the characteristic parameters, and evaluate the security of cross-platform data collaboration at the current stage according to the analysis results; Step S4: Predict the security of cross-platform data collaboration in the next stage according to the analysis results.

2. The network security data analysis method based on artificial intelligence according to claim 1, wherein The specific working process of step S2 includes: The characteristic parameters include: authentication parameters, data encryption parameters, and access transmission parameters; The authentication parameters include: password complexity parameters and multi-factor authentication parameters; The data encryption parameters include: encryption algorithm parameters and key management parameters; The access transmission parameters include: transmission protocol parameters and data integrity verification parameters.

3. The method for analyzing network security data based on artificial intelligence according to claim 2, characterized in that, The specific working process of step S3 includes: Step S31: Obtain the authentication, data encryption, and access transmission security coefficients respectively; Step S32: Obtain the cross-platform data collaboration security coefficient based on the authentication, data encryption, and access transmission security coefficients; Step S33: Compare the authentication, data encryption, and access transmission security coefficients with the system-set authentication, data encryption, and access transmission security coefficient thresholds respectively. If any security coefficient is greater than or equal to the system-set security coefficient threshold, it means that the security of cross-platform data collaboration is unqualified. If no security coefficient is greater than or equal to the system-set security coefficient threshold, go to step S34; Step S34: Compare the cross-platform data collaboration security coefficient with the system-set cross-platform data collaboration security coefficient threshold. If the cross-platform data collaboration security coefficient is greater than or equal to the system-set cross-platform data collaboration security coefficient threshold, it means that there is a potential risk in the security of cross-platform data collaboration.

4. The method for analyzing network security data based on artificial intelligence according to claim 3, wherein, The working process of obtaining the authentication security coefficient in step S31 includes: Obtain the authentication parameters, and build an authentication security coefficient calculation model based on the authentication parameters. The expression is: ； Wherein, is the identity verification safety factor, is the password complexity index coefficient, is the verification code index coefficient, and are the weight coefficients corresponding to the password complexity index coefficient and the verification code index coefficient respectively.

5. The method for analyzing network security data based on artificial intelligence according to claim 3, wherein The working process of obtaining the data encryption security coefficient in step S31 includes: Obtain the data encryption parameters, and build a data encryption security coefficient calculation model based on the data encryption parameters. The expression is: ； In the formula, is the data encryption security coefficient, is the encryption algorithm index coefficient, is the key management index coefficient, and are the weight coefficients corresponding to the encryption algorithm index coefficient and the key management index coefficient, respectively.

6. The method for analyzing network security data based on artificial intelligence according to claim 3, wherein, The working process of obtaining the access transmission security coefficient in step S31 includes: Obtain the access transmission parameters, and build an access transmission security coefficient calculation model based on the access transmission parameters. The expression is: ； Wherein, is the access transmission safety factor, is the transmission protocol index coefficient, is the data integrity verification index coefficient, and and are the weight coefficients corresponding to the transmission protocol index coefficient and the data integrity verification index coefficient respectively.

7. An artificial intelligence-based network security data analysis method according to claim 3, characterized in that The method for step S32 to obtain the cross-platform data collaboration security coefficient includes: Build a cross-platform data collaboration security coefficient calculation model. The expression is: ； Wherein, , and are the weight coefficients corresponding to the identity authentication security factor, the data encryption security factor, and the transmission security factor, respectively.

8. A method for network security data analysis based on artificial intelligence according to claim 1, characterized in that, The working process of step S4 includes: Obtain the current authentication parameters, data encryption parameters, and access transmission parameters, substitute them into the cross-platform data collaboration security coefficient calculation model, and obtain the current cross-platform data collaboration security coefficient , construct the cross-platform data collaboration security coefficient calculation model for the next stage, and the expression is: ； In the formula, After normalization processing, the value range is from 0 to 1, Is the cross-platform data collaboration security factor for the next stage, and the value range is from 0 to 1, Is the influence of unexpected factors. When there are no unexpected factors, W = 0. When there are unexpected factors and positive influences, W = 1. When there are unexpected factors and negative influences, W = -1, Is the self-regulation coefficient, and the stronger the self-regulation ability The larger; Input the cross-platform data collaboration security coefficient in the next stage into the trained artificial intelligence model to predict the cross-platform data collaboration security level in the next stage of the next stage.

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