Dynamic security authentication method for cross-platform information system integration

Abstract

The invention discloses a dynamic security authentication method for cross-platform information system integration, and relates to the technical field of cross-platform dynamic authentication. A non-tampering identity trust chain is established through a block chain consensus algorithm, the problems of single-point fault and expansibility of centralized identity management are solved, cross-platform unified identity verification is realized, the problem that multi-factor authentication lacks intelligent risk assessment is solved through intelligent contract automatic verification and user behavior analysis, threats can be dynamically responded, and the user experience is improved. The authority is dynamically adjusted based on the risk level, and a real-time access control decision engine is combined, so that the problem of response lag of traditional access control is solved, accurate authority control is realized, and finally, a continuously optimized security authentication mechanism and a unified and coordinated cross-platform security protection system are formed.

Description

Technical Field

[0001] This invention relates to the field of cross-platform dynamic authentication technology, specifically a dynamic security authentication method for cross-platform information system integration. Background Technology

[0002] With the deepening of digital transformation, cross-platform information systems have become the core infrastructure for modern enterprise and organizational operations, and the reliability of their security authentication mechanisms directly affects the security and stability of the entire digital ecosystem. In diverse application scenarios and complex network environments, traditional security authentication systems face unprecedented challenges. Current mainstream authentication solutions generally have significant shortcomings. Centralized identity management systems expose single points of failure and insufficient scalability when facing large-scale cross-platform applications, making it difficult to meet the reliability requirements of distributed environments. While existing multi-factor authentication mechanisms improve security, they lack intelligent risk assessment capabilities and cannot flexibly adjust according to the actual threat landscape. Traditional access control policies rely on preset rules and manual intervention, resulting in delayed responses to complex and ever-changing security threats and difficulty in achieving precise access control. Summary of the Invention

[0003] The purpose of this invention is to provide a dynamic security authentication method for cross-platform information system integration. By constructing a unified identity trust chain through blockchain, and using smart contracts and behavioral analysis, it achieves dynamic risk response, dynamic permission allocation, and a real-time decision engine to realize precise control.

[0004] The objective of this invention can be achieved through the following technical solutions: This application provides a dynamic security authentication method for cross-platform information system integration, including the following steps: By acquiring users' identity credentials across various platforms, establishing a distributed trust foundation through blockchain consensus algorithms, and storing the hash value and digital signature of the identity credentials in blockchain nodes, an immutable identity trust chain is formed, resulting in a unified cross-platform identity verification standard. The smart contract mechanism is used to automatically verify users' cross-platform access requests. If the identity credentials are verified through the blockchain network, a temporary access token is generated to obtain a trusted identity transfer mechanism between platforms. The user behavior data collection module is triggered by a temporary access token to monitor the user's login time, access frequency, operation path and device fingerprint behavior characteristics in real time. The behavior pattern recognition algorithm is used to analyze the user's historical behavior patterns and determine the user's baseline behavior profile. Establish a multi-dimensional behavioral feature vector, calculate the behavioral anomaly score through machine learning algorithms, and determine the risk level of user behavior; The dynamic permission allocation algorithm is launched based on the risk level score. According to the user's current risk status and the sensitivity level of the accessed resources, the user's access permission scope is adjusted in real time to obtain a dynamic permission configuration that matches the risk status. Establish a real-time access control decision engine to evaluate each user's resource access request in real time and determine accurate access control policies; Based on access control decision results and abnormal event records, threat intelligence analysis algorithms are used to update the user behavior pattern library and risk assessment model, resulting in a continuously optimized security authentication mechanism.

[0005] Furthermore, a unified cross-platform identity verification standard is obtained, specifically including: Obtain user identity credential information from various platforms, parse the identity data through a standardized protocol, generate a set of identity credentials containing user identifiers and attribute data, and use the SHA-256 algorithm to calculate the hash value of the identity credential set to generate a unique identifier hash value. The hash value is digitally signed using the elliptic curve digital signature algorithm to generate digital signature data containing the signature and public key. The digital signature data and hash value are broadcast to the blockchain nodes using the proof-of-work consensus algorithm and stored in the distributed ledger to obtain an immutable identity trust chain. If a cross-platform authentication request is received, the identity trust chain data is obtained from the blockchain node, the consistency of the digital signature and hash value is verified, the validity of the identity credential is determined, and based on the verification result, a cross-platform unified authentication standard is generated, stored in the blockchain node, and a standardized authentication output is obtained.

[0006] Furthermore, a trusted identity transfer mechanism between platforms is obtained, specifically including: The system employs a pre-established smart contract mechanism to obtain cross-platform access data from user requests, determines the validity of requests based on the rules defined in the smart contract through an automated verification process, and obtains confirmed request data from the verification process via a blockchain network. The identity credentials are verified using a distributed ledger to determine if they match the preset identity template. If the identity credentials are verified by the blockchain, a temporary access token is generated using an encryption algorithm. The token contains the scope of permissions requested by the user and the validity period, thus determining the content of the token. A temporary access token is obtained from the token generation step. The token is distributed to the target platform through a trusted identity transfer protocol. It is determined whether the token has been successfully transferred to each platform. The distributed temporary access token is obtained, and the access permission management module is used to match the access requests of the target platform. If a match is successful, the user's access permissions are determined through a permission allocation algorithm. Based on the permission allocation results, the user's access permission data is obtained, and the permission information is transmitted to each platform through a cross-platform interface protocol to obtain the access control results between platforms and to obtain a trusted identity transfer mechanism between platforms.

[0007] Furthermore, the baseline behavioral profile of the user is determined, specifically including: The system verifies the user's identity using a temporary access token, activates the data collection module, obtains the user's login time, access frequency, operation path, and device fingerprint, generates a real-time behavior dataset, processes the real-time behavior dataset using a random forest algorithm, extracts behavioral pattern features, and obtains the user's current behavior profile. If the similarity between the current behavior profile and the baseline behavior profile is lower than a preset threshold, the deviation between the two is calculated by Euclidean distance to determine the abnormal behavior point, and relevant operation paths and device fingerprints are extracted from historical behavior data to generate a behavior deviation sequence. The K-means clustering algorithm is used to classify the behavioral deviation sequence to obtain the category distribution of abnormal behavior. By combining the category distribution of abnormal behavior with login time and access frequency, it is determined whether the user behavior deviates from the normal pattern, and a behavioral risk score is generated. This score is used to adjust the permission level of temporary access tokens, obtain updated user behavior data, and determine the user's baseline behavioral profile.

[0008] Furthermore, determining the risk level of user behavior specifically includes: Acquire user behavior data, extract user interaction records from multi-source data streams, generate multi-dimensional behavior feature vectors, compare the behavior feature vectors with preset benchmark behavior profiles, calculate behavior deviation values, and if the behavior deviation value exceeds a preset threshold, trigger an abnormal behavior detection mechanism to extract abnormal behavior data. The support vector machine algorithm is used to analyze abnormal behavior data and calculate the abnormality score. The decision tree algorithm is applied to classify user behavior risk levels. High-risk behavior features are extracted from the risk level results and the baseline behavior profile is updated to optimize the calculation model of behavior deviation value and obtain more accurate deviation value.

[0009] Furthermore, obtain dynamic permission configurations that match the risk status, specifically including: Obtain user risk level scores, analyze user behavior data and historical access records, use support vector machine algorithm to determine the user's current risk status, combine with the sensitivity level of accessed resources, query a preset resource sensitivity level table, and determine resource access permission requirements; If a user's risk status is higher than a preset risk threshold, the permission adjustment rules are used to reduce the user's access permission level, resulting in a temporary permission configuration. Combined with the resource sensitivity level, a decision tree algorithm is used to generate a dynamic permission range that matches the risk status.

[0010] Furthermore, determine precise access control policies, specifically including: User authentication data and resource security classification information are obtained from the permission configuration information database. The correspondence between user permission level and resource security level is determined by using preset mapping rules, and the user permission level is obtained. The real-time decision engine obtains the resource access requests submitted by users, combines the user's permission level and resource security classification, and uses a matching judgment mechanism to determine whether the access conditions are met and obtain the matching results. If the matching result shows that the user's permission level is lower than the resource security level, the access denial operation is executed through the real-time decision engine, an access denial instruction is generated, and the relevant operation is recorded in the abnormal event log, which includes user authentication data, requested resource identifier and denial timestamp, to obtain an abnormal event log entry; By analyzing abnormal event log entries, patterns of frequent access denial are extracted, and a decision tree algorithm is used to update the permission configuration information database to generate an optimized access control policy. The updated permission level allocation rules are then extracted from the optimized access control policy. By combining resource security classification, the data is reloaded into the real-time decision engine to obtain updated decision rules. Based on these updated rules, the real-time decision engine dynamically evaluates subsequent resource access requests and generates precise access control policies.

[0011] Furthermore, the continuously optimized security authentication mechanism specifically includes: Obtain access control decision results and abnormal event records, use log analysis tools to preprocess the data, extract user operation time, operation type and abnormal flags to obtain a structured behavior dataset, extract user behavior features from the structured behavior dataset, use K-means clustering algorithm to classify user behavior, and determine normal behavior patterns and abnormal behavior patterns. Based on the categorized behavioral patterns, a threat intelligence analysis algorithm is used to compare the data with a pre-set attack pattern library. If a new attack pattern is detected, the attack pattern library is updated to obtain an expanded threat intelligence dataset. A decision tree algorithm is then used to update the risk assessment model to obtain an updated risk assessment result. If the risk assessment results exceed the preset threshold, the behavior recognition parameters are adjusted, the user behavior pattern library is updated, an optimized behavior pattern dataset is obtained, the permission allocation rules are dynamically adjusted using a rule generation algorithm, a new access control policy is generated, and an updated and continuously optimized security authentication mechanism is obtained.

[0012] Furthermore, after the security authentication mechanism is continuously optimized, it also includes: establishing a cross-platform security status synchronization mechanism to synchronize the user's risk assessment results and permission change information to various related platforms in real time. If a platform detects high-risk behavior, it will immediately notify other platforms to carry out corresponding security responses, thereby obtaining a unified and coordinated cross-platform security protection system.

[0013] Furthermore, a unified and coordinated cross-platform security protection system will be achieved, specifically including: By optimizing the security authentication mechanism, user identity data and behavior logs are obtained from various platforms. The data is processed using an encrypted transmission protocol to obtain standardized identity verification results. Combined with the user's risk assessment results, high-risk behavior information is encrypted and distributed to associated platforms, and the response status of each platform is obtained. Using message queue technology, after obtaining the response status from each platform, a permission change command is triggered to update the user's access permissions on all associated platforms. Once the permission change is confirmed, the platform behavior data after the permission change is confirmed is analyzed to determine whether there is any continuous abnormal behavior. If persistent abnormal behavior is detected, a security response command is generated according to the preset response strategy, distributed to each platform, and a unified protection operation is performed to obtain the protection execution result. Then, the security status record in the cross-platform synchronization system is updated to generate a unified and coordinated cross-platform security protection system.

[0014] The beneficial effects of this invention are as follows: By using a blockchain consensus algorithm, the hash values ​​and digital signatures of users' identity credentials across various platforms are stored on blockchain nodes, forming an immutable chain of identity trust. This solves the single point of failure risk and insufficient scalability issues of traditional centralized identity management systems. It not only establishes a unified cross-platform identity verification standard, making identity verification more reliable in a distributed environment, but also ensures the validity and consistency of identity credentials through standardized protocols for parsing identity data, hash calculation, and digital signature processes, thereby improving the security and stability of cross-platform identity authentication. The system employs a smart contract mechanism to automatically verify user cross-platform access requests. After identity credentials are verified through the blockchain network, a temporary access token is generated. Combined with user behavior data collection and analysis, it solves the problems of existing multi-factor authentication mechanisms lacking intelligent risk assessment capabilities and being unable to flexibly adjust according to the threat situation. By monitoring user login time, access frequency and other behavioral characteristics in real time, and using behavioral pattern recognition algorithms to determine the baseline behavioral profile, it can trigger anomaly detection and calculate the risk level when behavioral deviations exceed the threshold. This enables the authentication mechanism to dynamically respond to actual threats and enhances the risk prevention and control capabilities of cross-platform access. A dynamic permission allocation algorithm is launched based on risk level scoring, and user access permissions are adjusted in real time in conjunction with resource sensitivity levels. At the same time, a real-time access control decision engine is established to evaluate each resource access request in real time. This solves the problems of traditional access control policies that rely on preset rules and manual intervention, have delayed response, and are difficult to accurately manage permissions. When user permissions do not match the resource security level, the decision engine will refuse access and record abnormal events. It can also optimize policies by analyzing abnormal logs, forming a continuously optimized security authentication mechanism. Ultimately, dynamic permission configuration and precise access control that match the risk status are achieved, improving the efficiency of permission management and the level of security protection of cross-platform information systems. Attached Figure Description

[0015] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0016] Figure 1 A flowchart illustrating the dynamic security authentication method for cross-platform information system integration provided in this application; Figure 2 A flowchart illustrating the process of obtaining a unified cross-platform identity verification standard using the dynamic security authentication method for cross-platform information system integration provided in this application; Figure 3 A flowchart illustrating the process of obtaining dynamic permission configurations that match the risk status for the dynamic security authentication method for cross-platform information system integration provided in this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0018] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0019] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0020] Please see Figures 1-3 This embodiment provides a dynamic security authentication method for cross-platform information system integration, including the following steps: S1. Obtain user identity credential information from various platforms, establish a distributed trust foundation through blockchain consensus algorithm, store the hash value and digital signature of identity credential in blockchain nodes, form an immutable identity trust chain, and obtain a unified cross-platform identity verification standard.

[0021] Furthermore, a unified cross-platform identity verification standard is obtained, specifically including: S11. Obtain user identity credential information from each platform, parse the identity data through a standardized protocol, generate a set of identity credentials containing user identifiers and attribute data, calculate the hash value of the identity credential set using the SHA-256 algorithm, and generate a unique identifier hash value. S12. The hash value is digitally signed using the elliptic curve digital signature algorithm to generate digital signature data containing the signature and public key. The digital signature data and hash value are broadcast to the blockchain node using the proof-of-work consensus algorithm and stored in the distributed ledger to obtain an immutable identity trust chain. The process of digitally signing a hash value using the Elliptic Curve Digital Signature Algorithm (ECDSA) is implemented as follows: First, a private key on an elliptic curve is used to sign the hash value, generating a signature value. This signature value, together with the signer's public key, constitutes the digital signature data.

[0022] The specific steps include: selecting an elliptic curve parameter and a base point, calculating the hash value using the private key to obtain two signature parameters, which together with the public key constitute the complete digital signature data; when signature verification is required, the verifier uses the signer's public key and signature data, combined with the same elliptic curve parameter and hash value, to verify the validity of the signature using the ECDSA verification algorithm, thereby ensuring the integrity and authenticity of the signature data.

[0023] S13. If a cross-platform authentication request is received, obtain the identity trust chain data from the blockchain node, verify the consistency of the digital signature and hash value, determine the validity of the identity credential, generate a unified cross-platform authentication standard based on the verification result, store it in the blockchain node, and obtain a standardized authentication output.

[0024] Specifically, by using blockchain technology to build a distributed trust foundation, the problems of single point of failure risk, data tampering risk, and inconsistent identity verification standards in traditional identity management systems in cross-platform environments are solved. This not only ensures the authenticity and immutability of identity information, but also achieves standardization and consistency of cross-platform identity verification, improves the reliability and security of identity verification, and provides a more stable and reliable identity authentication foundation for cross-platform information systems.

[0025] S2. Based on the established unified identity verification standard, a smart contract mechanism is used to automatically verify users' cross-platform access requests. If the identity credentials are verified through the blockchain network, a temporary access token is generated to obtain a trusted identity transfer mechanism between platforms.

[0026] Furthermore, a trusted identity transfer mechanism between platforms is obtained, specifically including: The system employs a pre-established smart contract mechanism to obtain cross-platform access data from user requests, determines the validity of requests based on the rules defined in the smart contract through an automated verification process, and obtains confirmed request data from the verification process via a blockchain network. Among them, pre-established smart contracts are automatically executed contract terms deployed on the blockchain in the form of code. They can automatically execute contract terms when preset conditions are met, without human intervention. In cross-platform access control scenarios, smart contract mechanisms can automate the verification of user access requests.

[0027] The rules defined by smart contracts are implemented through pre-written code logic deployed on the blockchain. When a user initiates a cross-platform access request, the smart contract automatically receives the request data and then verifies the request one by one according to the pre-defined rules in the contract (such as identity verification, permission checks, and time validity). For example, it checks whether the identity credentials submitted by the user are consistent with the information recorded on the blockchain, verifies whether the user's requested permissions are within their authorized scope, and whether the request is within the validity period of the permissions. If all rules are met, the smart contract will determine that the request is valid and execute the corresponding operation, such as generating a temporary access token. If any rule is not met, the request is rejected and an error message is returned. This automated judgment process utilizes the decentralized and tamper-proof characteristics of the blockchain to ensure the fairness and security of the verification, and improves the efficiency and reliability of cross-platform access control.

[0028] The identity credentials are verified using a distributed ledger to determine if they match the preset identity template. If the identity credentials are verified by the blockchain, a temporary access token is generated using an encryption algorithm. The token contains the scope of permissions requested by the user and the validity period, thus determining the content of the token. A temporary access token is obtained from the token generation step. The token is distributed to the target platform through a trusted identity transfer protocol. It is determined whether the dynamic transfer to each platform has been successfully completed. The distributed temporary access token is obtained, and the access permission management module is used to match the access requests of the target platform. If a match is successful, the user's access permissions are determined through a permission allocation algorithm. Based on the permission allocation results, the user's access permission data is obtained, and the permission information is transmitted to each platform through a cross-platform interface protocol to obtain the access control results between platforms and to obtain a trusted identity transfer mechanism between platforms.

[0029] Specifically, by adopting smart contract mechanisms and blockchain technology, the problems of complex and inefficient identity verification and unreliable identity information transmission in cross-platform access are solved. This not only improves the efficiency and security of cross-platform access, but also ensures the reliable transmission of identity information between different platforms and establishes a reliable identity transmission mechanism between platforms.

[0030] S3. Trigger the user behavior data collection module through temporary access tokens to monitor the user's login time, access frequency, operation path and device fingerprint behavior characteristics in real time. Use behavior pattern recognition algorithms to analyze the user's historical behavior patterns and determine the user's baseline behavior profile.

[0031] Furthermore, the baseline behavioral profile of the user is determined, specifically including: The system verifies the user's identity using a temporary access token, activates the data collection module, obtains the user's login time, access frequency, operation path, and device fingerprint, generates a real-time behavior dataset, processes the real-time behavior dataset using a random forest algorithm, extracts behavioral pattern features, and obtains the user's current behavior profile. The process involves using a real-time behavior dataset (including user login time, access frequency, operation path, and device fingerprint information) as input data. Then, multiple decision trees using the random forest algorithm are employed to classify and select features from the data. Each decision tree is trained by randomly sampling a subset of the dataset, generating different feature importance assessments. Finally, by summarizing the results of all decision trees, the most distinctive features for user behavior patterns are identified, such as specific operation path patterns or frequent login intervals. This process extracts key features that effectively characterize user behavior patterns, providing a foundation for subsequent behavior analysis and risk assessment.

[0032] If the similarity between the current behavior profile and the baseline behavior profile is lower than a preset threshold, the deviation between the two is calculated by Euclidean distance to determine the abnormal behavior point, and relevant operation paths and device fingerprints are extracted from historical behavior data to generate a behavior deviation sequence. The K-means clustering algorithm is used to classify the behavioral deviation sequence to obtain the category distribution of abnormal behavior. By combining the category distribution of abnormal behavior with login time and access frequency, it is determined whether the user behavior deviates from the normal pattern, and a behavioral risk score is generated. This score is used to adjust the permission level of temporary access tokens, obtain updated user behavior data, and determine the user's baseline behavioral profile.

[0033] Specifically, by monitoring and analyzing users' login time, access frequency, operation path, and device fingerprints in real time, it solves the problem that traditional security authentication cannot dynamically perceive and assess user behavior risks. It can dynamically identify whether user behavior deviates from the normal pattern and adjust the permission level of temporary access tokens accordingly, thereby achieving real-time monitoring and risk assessment of user behavior, enhancing the security and flexibility of cross-platform information systems, and effectively preventing potential threats from abnormal behavior.

[0034] S4. Based on the user's baseline behavior profile, establish a multi-dimensional behavior feature vector. If the deviation between the current user behavior and the baseline behavior profile exceeds a preset threshold, trigger the abnormal behavior detection mechanism. Calculate the behavior abnormality score through machine learning algorithms to determine the risk level of the user behavior.

[0035] Furthermore, determining the risk level of user behavior specifically includes: Acquire user behavior data, extract user interaction records from multi-source data streams, generate multi-dimensional behavior feature vectors, compare the behavior feature vectors with preset benchmark behavior profiles, calculate behavior deviation values, and if the behavior deviation value exceeds a preset threshold, trigger an abnormal behavior detection mechanism to extract abnormal behavior data. The support vector machine algorithm is used to analyze abnormal behavior data and calculate the abnormality score. The decision tree algorithm is applied to classify user behavior risk levels. High-risk behavior features are extracted from the risk level results and the baseline behavior profile is updated to optimize the calculation model of behavior deviation value and obtain more accurate deviation value.

[0036] When using the Support Vector Machine (SVM) algorithm to analyze abnormal behavior data, the extracted abnormal behavior data is first used as input features. Through training the SVM model, its powerful classification ability is utilized to divide the data into different categories, thereby identifying the characteristic patterns of abnormal behavior and calculating an anomaly score, which reflects the degree to which the behavior deviates from the normal pattern. Subsequently, the decision tree algorithm is applied to classify the risk level of user behavior according to the anomaly score and preset rules and thresholds, classifying user behavior into different levels such as low risk, medium risk, and high risk, so as to take corresponding safety measures. This process combines the accurate classification ability of SVM and the rule-based decision-making advantage of decision trees to achieve an accurate assessment of the risk level of user behavior.

[0037] Specifically, by establishing multi-dimensional behavioral feature vectors and combining them with machine learning algorithms, the problem of insufficient accuracy in detecting abnormal user behavior and the inability to dynamically assess behavioral risks in traditional security authentication is solved. It can not only accurately identify the degree of abnormality in user behavior, but also dynamically update the baseline behavioral profile based on high-risk behavioral characteristics and optimize the calculation model of behavioral deviation values, thereby achieving real-time dynamic assessment of user behavior risks and effectively improving the security and risk response capabilities of cross-platform information systems.

[0038] S5. Using the risk level scoring results, a dynamic permission allocation algorithm is activated. Based on the user's current risk status and the sensitivity level of the accessed resources, the user's access permission range is adjusted in real time. If the risk score is high, the permission level is reduced to obtain a dynamic permission configuration that matches the risk status.

[0039] Furthermore, obtain dynamic permission configurations that match the risk status, specifically including: S51. Obtain the user's risk level score. By analyzing user behavior data and historical access records, and using the support vector machine algorithm, determine the user's current risk status. Combined with the sensitivity level of the accessed resources, query the preset resource sensitivity level table to determine the resource access permission requirements. S52. If a user's risk status is higher than a preset risk threshold, the permission adjustment rules are used to reduce the user's access permission level and obtain a temporary permission configuration. Combined with the resource sensitivity level, a decision tree algorithm is used to generate a dynamic permission range that matches the risk status.

[0040] In this process, relevant features are input into a decision tree model based on the user's risk level score and the sensitivity level of the resources. The decision tree classifies the user's risk status through pre-defined rules and conditional judgment nodes, and determines the corresponding permission adjustment strategy based on the classification results. For example, for high-risk users, the decision tree may restrict their access to sensitive resources; for low-risk users, higher permissions are maintained. Ultimately, the decision tree outputs a dynamic permission range that matches the user's current risk status, thereby achieving the goal of flexibly adjusting user access permissions according to risk status.

[0041] Specifically, the dynamic permission allocation algorithm solves the problem of fixed permissions in traditional access control and the inability to adjust permissions in real time according to user behavior risks. It can dynamically adjust access permissions according to the user's real-time risk status, ensuring that high-risk users are restricted from accessing sensitive resources, thereby improving the security and flexibility of cross-platform information systems and effectively preventing potential security threats.

[0042] S6. By using dynamic permission configuration information, a real-time access control decision engine is established to evaluate each user's resource access request in real time. If the user's permission level does not match the security level of the requested resource, access is denied and abnormal events are recorded to determine a precise access control policy.

[0043] Furthermore, determine precise access control policies, specifically including: User authentication data and resource security classification information are obtained from the permission configuration information database. The correspondence between user permission level and resource security level is determined by using preset mapping rules, and the user permission level is obtained. The real-time decision engine obtains the resource access requests submitted by users, combines the user's permission level and resource security classification, and uses a matching judgment mechanism to determine whether the access conditions are met and obtain the matching results. If the matching result shows that the user's permission level is lower than the resource security level, the access denial operation is executed through the real-time decision engine, an access denial instruction is generated, and the relevant operation is recorded in the abnormal event log, which includes user authentication data, requested resource identifier and denial timestamp, to obtain an abnormal event log entry; By analyzing abnormal event log entries, patterns of frequent access denial are extracted, and a decision tree algorithm is used to update the permission configuration information database to generate an optimized access control policy. The updated permission level allocation rules are then extracted from the optimized access control policy. By combining resource security classification, the data is reloaded into the real-time decision engine to obtain updated decision rules. Based on these updated rules, the real-time decision engine dynamically evaluates subsequent resource access requests and generates precise access control policies.

[0044] Specifically, by establishing a real-time access control decision engine, the problems of insufficient flexibility in permission management, inability to respond to changes in user behavior in real time, and difficulty in accurately matching resource access needs in traditional access control policies are solved. It can adjust access control policies in real time and dynamically, ensuring that user permissions and resource security levels are accurately matched, improving the security and flexibility of cross-platform information systems, and effectively preventing unauthorized access.

[0045] S7. Based on access control decision results and abnormal event records, update the user behavior pattern library and risk assessment model using threat intelligence analysis algorithms. If new attack patterns or abnormal behavior types are detected, automatically adjust behavior recognition parameters and permission allocation rules to obtain a continuously optimized security authentication mechanism.

[0046] Furthermore, the continuously optimized security authentication mechanism specifically includes: Obtain access control decision results and abnormal event records, use log analysis tools to preprocess the data, extract user operation time, operation type and abnormal flags to obtain a structured behavior dataset, extract user behavior features from the structured behavior dataset, use K-means clustering algorithm to classify user behavior, and determine normal behavior patterns and abnormal behavior patterns. Based on the categorized behavioral patterns, a threat intelligence analysis algorithm is used to compare the data with a pre-set attack pattern library. If a new attack pattern is detected, the attack pattern library is updated to obtain an expanded threat intelligence dataset. A decision tree algorithm is then used to update the risk assessment model to obtain an updated risk assessment result. If the risk assessment results exceed the preset threshold, the behavior recognition parameters are adjusted, the user behavior pattern library is updated, an optimized behavior pattern dataset is obtained, the permission allocation rules are dynamically adjusted using a rule generation algorithm, a new access control policy is generated, and an updated and continuously optimized security authentication mechanism is obtained.

[0047] Specifically, by employing threat intelligence analysis algorithms and machine learning techniques, the system addresses the shortcomings of traditional security authentication mechanisms in detecting new attack patterns and abnormal behaviors, as well as their inability to dynamically optimize. It can automatically identify and respond to new security threats, continuously optimize the security authentication mechanism, improve the system's security and adaptability, and ensure the long-term stable operation of cross-platform information systems.

[0048] Furthermore, after the security authentication mechanism is continuously optimized, it also includes: establishing a cross-platform security status synchronization mechanism to synchronize the user's risk assessment results and permission change information to various related platforms in real time. If a platform detects high-risk behavior, it will immediately notify other platforms to carry out corresponding security responses, thereby obtaining a unified and coordinated cross-platform security protection system.

[0049] Furthermore, a unified and coordinated cross-platform security protection system will be achieved, specifically including: By optimizing the security authentication mechanism, user identity data and behavior logs are obtained from various platforms. The data is processed using an encrypted transmission protocol to obtain standardized identity verification results. Combined with the user's risk assessment results, high-risk behavior information is encrypted and distributed to associated platforms, and the response status of each platform is obtained. Using message queue technology, after obtaining the response status from each platform, a permission change command is triggered to update the user's access permissions on all associated platforms. Once the permission change is confirmed, the platform behavior data after the permission change is confirmed is analyzed to determine whether there is any continuous abnormal behavior. If persistent abnormal behavior is detected, a security response command is generated according to the preset response strategy, distributed to each platform, and a unified protection operation is performed to obtain the protection execution result. Then, the security status record in the cross-platform synchronization system is updated to generate a unified and coordinated cross-platform security protection system.

[0050] Specifically, by establishing a cross-platform security status synchronization mechanism, the problem of isolated security information between platforms and the inability to coordinate real-time responses to security threats in traditional cross-platform information systems has been solved. Real-time information sharing and collaborative response between platforms have been achieved, and a unified and coordinated cross-platform security protection system has been constructed, effectively improving the security of the entire system and its ability to respond to complex threats.

[0051] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A dynamic security authentication method for cross-platform information system integration, characterized by: The method comprises the following steps: Obtain the identity credential information of the user on each platform, establish a distributed trust basis through a blockchain consensus algorithm, store the hash value and digital signature of the identity credential in the blockchain node, form an unalterable identity trust chain, and obtain a unified identity verification standard across platforms; Adopt a smart contract mechanism to automatically verify the user's cross-platform access request, generate a temporary access token if the identity credential is verified by the blockchain network, and obtain a trusted identity transfer mechanism between platforms; Trigger the user behavior data collection module through the temporary access token, monitor the user's login time, access frequency, operation path, and device fingerprint behavior characteristics in real time, analyze the user's historical behavior patterns using a behavior pattern recognition algorithm, and determine the user's baseline behavior profile; Establish a multi-dimensional behavior feature vector, calculate the behavior anomaly score using a machine learning algorithm, and determine the risk level of the user's behavior; Start a dynamic permission allocation algorithm using the risk level score result, adjust the user's access permission range in real time according to the user's current risk state and the sensitivity level of the accessed resources, and obtain a dynamic permission configuration that matches the risk state; Establish a real-time access control decision engine to evaluate the user's resource access request in real time and determine the precise access control strategy; Update the user behavior pattern library and risk assessment model using a threat intelligence analysis algorithm based on the access control decision result and abnormal event record, and obtain a continuously optimized security authentication mechanism.

2. The dynamic security authentication method for cross-platform information system integration of claim 1, wherein: A unified identity verification standard across platforms is obtained, specifically including: Obtain the identity credential information of the user on each platform, parse the identity data through a standardized protocol, generate an identity credential set containing user identification and attribute data, calculate the hash value of the identity credential set using the SHA-256 algorithm, and generate a unique identification hash value; Perform digital signature on the hash value using the elliptic curve digital signature algorithm, generate digital signature data containing the signature and public key, broadcast the digital signature data and hash value to the blockchain node using the proof-of-work consensus algorithm, store it in the distributed ledger, and obtain an unalterable identity trust chain; If a cross-platform identity verification request is received, obtain the identity trust chain data from the blockchain node, verify the consistency of the digital signature and hash value, determine the validity of the identity credential, generate a unified identity verification standard across platforms based on the verification result, store it in the blockchain node, and obtain a standardized identity verification output.

3. The dynamic security authentication method for cross-platform information system integration of claim 1, wherein: A trusted identity transfer mechanism between platforms is obtained, specifically including: Adopt a pre-established smart contract mechanism to obtain cross-platform access data from user requests, determine the validity of the request based on the rules defined by the smart contract, and obtain confirmed request data from the verification process through the blockchain network; Check the identity credential against the pre-set identity template to obtain a check result, generate a temporary access token using an encryption algorithm if the identity credential passes the blockchain verification, and the token contains the permission range and validity period of the user's request, and determine the token content; The temporary access token is obtained from the token generation step, the token is distributed to the target platform through the trusted identity transfer protocol, it is judged whether the token is successfully transferred to each platform, the distributed temporary access token is obtained, and the access request of the target platform is matched by using an access permission management module; If the matching is successful, the user access permission is determined by using a permission allocation algorithm, the user access permission data is obtained according to the permission allocation result, the permission information is transmitted to each platform through a cross-platform interface protocol, the access control result between platforms is obtained, and the trusted identity transfer mechanism between platforms is obtained.

4. The dynamic security authentication method for cross-platform information system integration of claim 1, wherein: The reference behavior profile of the user is determined, specifically including: The user identity is verified through the temporary access token, the data acquisition module is activated, the user login time, access frequency, operation path and device fingerprint are obtained, the real-time behavior data set is generated, the random forest algorithm is used to process the real-time behavior data set, the behavior mode feature is extracted, and the current behavior profile of the user is obtained; If the similarity between the current behavior profile and the reference behavior profile is lower than a preset threshold, the deviation between the two is calculated by using the Euclidean distance, the abnormal behavior point is determined, the related operation path and device fingerprint are extracted from the historical behavior data, and the behavior deviation sequence is generated; The K-means clustering algorithm is used to classify the behavior deviation sequence, the category distribution of the abnormal behavior is obtained, whether the user behavior deviates from the normal mode is judged by combining the login time and the access frequency according to the category distribution of the abnormal behavior, the behavior risk score is generated, which is used to adjust the permission level of the temporary access token, the updated user behavior data is obtained, and the reference behavior profile of the user is determined.

5. The dynamic security authentication method for cross-platform information system integration of claim 1, wherein: The risk level of the user behavior is judged, specifically including: The user behavior data is obtained, the user interaction record is extracted from the multi-source data stream, the multi-dimensional behavior feature vector is generated, the behavior feature vector is compared by using the preset reference behavior profile, the behavior deviation value is calculated, if the behavior deviation value exceeds the preset threshold, the abnormal behavior detection mechanism is triggered, and the abnormal behavior data is extracted; The support vector machine algorithm is used to analyze the abnormal behavior data, the abnormality score is calculated, the decision tree algorithm is applied, the risk level of the user behavior is divided, the high-risk behavior feature is extracted from the risk level result, the reference behavior profile is updated, which is used to optimize the calculation model of the behavior deviation value, and a more accurate deviation value is obtained.

6. The dynamic security authentication method for cross-platform information system integration of claim 1, wherein: A dynamic permission configuration matching the risk state is obtained, specifically including: The user risk level score is obtained, the user behavior data and the historical access record are analyzed, the support vector machine algorithm is used to determine the current risk state of the user, the resource access permission requirement is judged by combining the sensitive level of the accessed resource and querying the preset resource sensitive level table; If the user risk state is higher than the preset risk threshold, the user access permission level is reduced by using the permission adjustment rule, the temporary permission configuration is obtained, and the dynamic permission range matching the risk state is generated by using the decision tree algorithm in combination with the resource sensitive level.

7. The dynamic security authentication method for cross-platform information system integration of claim 1, wherein: A precise access control strategy is determined, specifically including: The user identity verification data and the resource security classification information are obtained from the permission configuration information library, the correspondence between the user permission level and the resource security level is determined by using a preset mapping rule, and the user permission level is obtained. The real-time decision engine obtains a resource access request submitted by a user, combines a user permission level and a resource security classification, adopts a matching judgment mechanism, judges whether an access condition is met, and obtains a matching result. If the matching result shows that the user permission level is lower than the resource security level, the real-time decision engine executes a denial of access operation, generates a denial of access instruction, records relevant operations to an abnormal event log, includes user identity verification data, a requested resource identifier, and a denial timestamp, and obtains an abnormal event log entry. By analyzing the abnormal event log entry, a frequently denied access pattern is extracted, a decision tree algorithm is adopted to update a permission configuration information library, an optimized access control strategy is generated, and an updated permission level allocation rule is extracted from the optimized access control strategy. The resource security classification is combined, reloaded to the real-time decision engine, and an updated decision rule is obtained. Based on the updated decision rule, the real-time decision engine dynamically evaluates subsequent resource access requests, and generates a precise access control strategy.

8. The dynamic security authentication method for cross-platform information system integration of claim 1, wherein: A continuously optimized security authentication mechanism is obtained, specifically including: Access control decision results and abnormal event records are obtained, a log analysis tool is used to preprocess data, user operation time, operation type, and abnormal flag are extracted, a structured behavior data set is obtained, user behavior features are extracted from the structured behavior data set, a K-means clustering algorithm is used to classify user behaviors, and normal behavior patterns and abnormal behavior patterns are determined. According to the classified behavior patterns, a threat intelligence analysis algorithm is used to compare a preset attack pattern library. If a new attack pattern is detected, the attack pattern library is updated, an extended threat intelligence data set is obtained, a decision tree algorithm is used to update a risk assessment model, and an updated risk assessment result is obtained. If the risk assessment result exceeds a preset threshold, the behavior recognition parameters are adjusted, the user behavior pattern library is updated, the optimized behavior pattern data set is obtained, the rule generation algorithm is used to dynamically adjust the permission allocation rule, the new access control strategy is generated, and the continuously updated security authentication mechanism is obtained.

9. The dynamic security authentication method for cross-platform information system integration of claim 8, wherein: After obtaining the continuously optimized security authentication mechanism, a cross-platform security state synchronization mechanism is established, the user risk assessment result and the permission change information are synchronized to each platform in real time, if a high-risk behavior is detected on a certain platform, other platforms are immediately notified to perform corresponding security responses, and a unified and coordinated cross-platform security protection system is obtained.

10. The dynamic security authentication method for cross-platform information system integration of claim 9, wherein: A unified and coordinated cross-platform security protection system is obtained, specifically including: Through the optimized security authentication mechanism, user identity data and behavior logs are obtained from each platform, an encryption transmission protocol is used to process the data, a standardized identity verification result is obtained, high-risk behavior information is encrypted and distributed to associated platforms in combination with the user risk assessment result, and the response state of each platform is obtained. After obtaining the response state of each platform, the message queue technology is adopted, the permission change instruction is triggered, the access permission of the user on all associated platforms is updated, the permission change confirmation is obtained, and whether there is a continuous abnormal behavior is judged by analyzing the platform behavior data after the permission change confirmation. If the continuous abnormal behavior is detected, a security response instruction is generated through a preset response strategy, distributed to each platform, and a unified protection operation is performed to obtain a protection execution result, and the security state record in the cross-platform synchronization system is updated to generate a unified coordinated cross-platform security protection system.

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