CN117319030A - Data safety transmission system - Google Patents

Data safety transmission system Download PDF

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Publication number
CN117319030A
CN117319030A CN202311261908.3A CN202311261908A CN117319030A CN 117319030 A CN117319030 A CN 117319030A CN 202311261908 A CN202311261908 A CN 202311261908A CN 117319030 A CN117319030 A CN 117319030A
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module
data
sub
report
key
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柳晶
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Beijing Haitai Fangyuan High Technology Co Ltd
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Beijing Haitai Fangyuan High Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
    • H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
    • H04L63/0435—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload wherein the sending and receiving network entities apply symmetric encryption, i.e. same key used for encryption and decryption
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    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
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    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
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    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
    • G06F21/577—Assessing vulnerabilities and evaluating computer system security
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/602—Providing cryptographic facilities or services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
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    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
    • H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
    • H04L63/0442—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload wherein the sending and receiving network entities apply asymmetric encryption, i.e. different keys for encryption and decryption
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Abstract

The invention relates to the technical field of network security, in particular to a data security transmission system which comprises an identity verification module, a key management module, a privacy enhancement module, an audit and compliance module, a deep security analysis module, a blockchain technology module, a behavior analysis module and a data fusion module. In the invention, multi-factor identity verification is combined with an advanced hash algorithm, a neural network and a hardware token, an identity verification mechanism is provided, key management adopts elliptic curve encryption and blockchain, the life cycle of a key and data guarantee are optimized, homomorphic encryption and differential privacy strategies ensure data confidentiality and privacy safety, deep learning safety analysis uses a convolutional neural network and a decision tree, loopholes and anomalies are positioned, blockchain technology guarantees data integrity and traceability, intelligent behavior analysis is combined with SVM and random forest, anomaly is identified and threat is predicted, and data fusion supports integration across fields and platforms, so that diversified safety requirements are met.

Description

Data safety transmission system
Technical Field
The invention relates to the technical field of network security, in particular to a data security transmission system.
Background
The field of network security technology refers to protecting computer networks and data in the networks from security threats such as unauthorized access, data leakage, malicious attacks and the like by various technical means. It covers a wide range of fields including network communications, data transmission, authentication, encryption and decryption, access control, etc. The data security transmission system is a system specially designed for protecting the security of data in the network transmission process. The aim is to ensure that data can be safely transferred to an intended receiver during data transmission, and data leakage, tampering, hijacking or unauthorized access are prevented.
The data security transmission system uses an encryption algorithm to convert data into ciphertext, and only a legal receiver can decrypt the data if the legal receiver has a decryption key. In addition, the system ensures confidentiality, integrity and availability in the data transmission process through authentication, access control, security protocols and other mechanisms. The configuration of the firewall and intrusion detection system then helps to monitor and prevent network attacks, malware, and unauthorized access.
In the existing data security transmission system, the existing system usually only uses a password or a one-time token for identity verification, so that the security is weak and the system is easy to attack. Most existing systems do not perform life cycle management of keys, resulting in insufficient security of key management. The privacy protection of the data in the existing system is often insufficient, and the auditing and compliance aspects may not be as automatic and accurate as the system, so that problems are more likely to occur in the face of audit timing. Secondly, the existing system may only perform basic anomaly detection on security analysis, and hidden security threats are difficult to find. Without using blockchain techniques, the integrity and traceability of the data is inadequate, making it difficult to discover when the data is tampered with. The existing system only depends on rules or basic models to conduct behavior analysis, and accuracy and depth are limited. Finally, in existing systems, cross-domain data fusion capability is often lacking, and complex data input and security requirements are difficult to deal with.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a data security transmission system.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the data security transmission system comprises an identity verification module, a key management module, a privacy enhancement module, an auditing and compliance module, a deep security analysis module, a blockchain technology module, a behavior analysis module and a data fusion module;
the identity verification module adopts a multi-factor verification method, performs identity authentication by combining a password, a neural network model of biological feature recognition and a dynamic matching technology of a hardware token through an advanced hash algorithm, and generates an identity verification report;
the key management module uses an elliptic curve-based encryption algorithm and a blockchain technology according to the identity verification report to ensure the safe life cycle management of the key and produce an encrypted key token;
the privacy enhancement module utilizes an encryption key token, combines secret sharing and zero knowledge proof, and adopts homomorphic encryption and differential privacy technology to ensure data privacy to obtain privacy enhanced data;
the audit and compliance block is matched with the policy based on privacy enhancement data by adopting an automatic script, performs compliance checking, builds an audit log and generates audit and compliance reports;
the deep security analysis module automatically discovers security holes by utilizing audit and compliance reports and applying a convolutional neural network and a decision tree algorithm, detects abnormal behaviors and compiles security analysis reports;
the block chain technology module forms a block chain data record by using an intelligent contract and Merkle tree technology according to the security analysis report;
the behavior analysis module is used for carrying out deep analysis on network traffic by utilizing an SVM algorithm and a random forest model based on the blockchain data record, identifying abnormal network behaviors and generating a behavior analysis report;
and the data fusion module is used for realizing the transmission fusion of the safety data by combining the principal component analysis and the depth self-encoder according to the behavior analysis report and outputting the fused safety data.
As a further aspect of the invention: the identity verification module comprises a code sub-module, a biological characteristic sub-module and a hardware token module;
the key management module comprises a key generation sub-module, a key storage sub-module and a key updating sub-module;
the privacy enhancement module comprises a homomorphic encryption sub-module, a differential privacy sub-module and an attribute encryption sub-module;
the audit and compliance block comprises a log record sub-module, a compliance check sub-module and a report generation sub-module;
the deep security analysis module comprises a vulnerability discovery sub-module, an anomaly detection sub-module and a threat prediction sub-module;
the block chain technology module comprises a data uploading sub-module, a data verifying sub-module and a data tracking sub-module;
the behavior analysis module comprises a flow monitoring sub-module, a behavior modeling sub-module and a threat prevention sub-module;
the data fusion module comprises a data encryption sub-module, a feature extraction sub-module and a data aggregation sub-module.
As a further aspect of the invention: the password submodule adopts a secure hash algorithm to carry out deep encryption processing on a password input by a user, and generates an encrypted password report;
the biological characteristic sub-module carries out accurate biological characteristic identification and matching by adopting a convolutional neural network based on the encrypted password report to generate a biological characteristic verification report;
the hardware token module combines the biological characteristic verification report, dynamically verifies the validity of the hardware token by using an OTP cryptographic technology, and generates an identity verification report.
As a further aspect of the invention: the key generation sub-module establishes an encryption key by adopting elliptic curve encryption technology based on the identity verification report to generate a key generation report;
the key storage submodule is used for storing the key in the blockchain in a lasting mode by using the Merker Partrey tree technology according to the key generation report to generate a key storage record;
the key update sub-module: and according to the key storage record, the timeliness and the security of the key are ensured through a PFS forward secret technology, and an encryption key token is generated.
As a further aspect of the invention: the homomorphic encryption sub-module uses an encryption key token, and utilizes FHE homomorphic encryption technology to ensure privacy in the data processing process, so as to generate homomorphic encryption data;
the differential privacy submodule applies a Laplace mechanism to conduct differential privacy processing aiming at homomorphic encrypted data to generate differential privacy data;
and the attribute encryption submodule implements an ABE attribute-based encryption technology to enhance the privacy of the data according to the differential privacy data, and generates privacy enhanced data.
As a further aspect of the invention: the log recording submodule monitors the operation of the privacy enhanced data, and uses a system log protocol to carry out log recording to generate an operation log;
the compliance checking sub-module performs policy matching for compliance checking by combining a regular expression technology aiming at the operation log to generate a compliance checking report;
the report generation submodule generates audit and compliance reports by integrating and formatting audit and compliance data with BI Tools business intelligence Tools in dependence upon the compliance check report.
As a further aspect of the invention: the vulnerability discovery submodule adopts a convolutional neural network to perform feature extraction and image classification based on audit and compliance reports, automatically scans and locates potential security vulnerabilities, and generates a vulnerability location report;
the abnormality detection sub-module performs feature selection and decision rule generation by applying a decision tree algorithm according to the vulnerability positioning report, realizes automatic detection of abnormal behaviors and generates an abnormal behavior report;
and the threat prediction sub-module predicts the security threat through a probability model according to the abnormal behavior report and the historical data, and generates a security analysis report.
As a further aspect of the invention: the data uplink sub-module automatically writes the security analysis result into the blockchain by using an intelligent contract based on the security analysis report to generate an uplink data record;
the data verification sub-module performs integrity and consistency verification of data by reading the uplink data record and applying the Merkle tree technology to generate a data verification report;
the data tracking submodule tracks the change and circulation of data in the blockchain by adopting a timestamp and a hash function according to the data verification report, and generates a blockchain data record.
As a further aspect of the invention: the flow monitoring submodule carries out flow characteristic classification by adopting a support vector machine based on the blockchain data record to generate a flow classification report;
the behavior modeling submodule performs feature screening and classification by utilizing a random forest model according to the flow classification report to generate a behavior modeling result;
and the threat prevention sub-module is used for identifying the network behavior and preventing potential threats according to the behavior modeling result by applying a rule engine and threshold setting, and generating a behavior analysis report.
As a further aspect of the invention: the data encryption sub-module carries out safe transmission encryption processing on data by adopting a symmetrical or asymmetrical encryption algorithm based on the behavior analysis report to generate encrypted data;
the feature extraction submodule applies principal component analysis to carry out high-dimensional data dimension reduction and feature extraction based on the encrypted data to generate a feature data set;
and the data aggregation submodule encodes and decodes the data characteristics by utilizing a depth self-encoder according to the characteristic data set to generate fused safety data.
Compared with the prior art, the invention has the advantages and positive effects that:
in the invention, the multi-factor authentication combines an advanced hash algorithm, a neural network model and a hardware token technology, and provides a stronger authentication mechanism. The enhanced key management utilizes an elliptic curve-based encryption algorithm and a blockchain technology, so that the life cycle management and the data security of the key are improved. Advanced privacy protection adopts homomorphic encryption and differential privacy technology to protect the encryption and privacy security of data. Audit and compliance improve compliance inspection efficiency through automated script and policy matching, and facilitate future audit. The deep learning security analysis utilizes convolutional neural networks and decision tree algorithms to accurately identify security vulnerabilities and abnormal behaviors. Blockchain technology ensures data integrity and traceability, increasing system transparency. The intelligent behavior analysis utilizes SVM and random forest algorithms to identify abnormal behavior and predict security threats. The data fusion capability allows cross-domain and cross-platform data fusion, adapting to diversified data input and security requirements.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a schematic diagram of a system framework of the present invention;
FIG. 3 is a flowchart of an authentication module according to the present invention;
FIG. 4 is a flow chart of a key management module of the present invention;
FIG. 5 is a flow chart of a privacy enhancement module of the present invention;
FIG. 6 is a block flow diagram of audit and co-scaling of the present invention;
FIG. 7 is a flow chart of a deep security analysis module according to the present invention;
FIG. 8 is a block chain technology module flow diagram of the present invention;
FIG. 9 is a flow chart of a behavior analysis module according to the present invention;
FIG. 10 is a flow chart of a data fusion module according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1
Referring to fig. 1, a data security transmission system includes an identity verification module, a key management module, a privacy enhancement module, an audit and compliance module, a deep security analysis module, a blockchain technology module, a behavior analysis module and a data fusion module;
the identity verification module adopts a multi-factor verification method, performs identity authentication by combining a password, a neural network model of biological feature recognition and a dynamic matching technology of a hardware token through an advanced hash algorithm, and generates an identity verification report;
the key management module uses an elliptic curve-based encryption algorithm and a blockchain technology according to the identity verification report to ensure the safe life cycle management of the key and produce an encrypted key token;
the privacy enhancement module utilizes an encryption key token, combines secret sharing and zero knowledge proof, and adopts homomorphic encryption and differential privacy technology to ensure data privacy so as to obtain privacy enhanced data;
the audit and compliance block is matched with the policy based on privacy enhancement data by adopting an automatic script, performs compliance checking, builds an audit log and generates audit and compliance reports;
the deep security analysis module automatically discovers security holes by utilizing audit and compliance reports and applying a convolutional neural network and a decision tree algorithm, detects abnormal behaviors and compiles security analysis reports;
the block chain technology module forms a block chain data record by using an intelligent contract and Merkle tree technology according to the security analysis report;
the behavior analysis module is used for deeply analyzing the network flow based on the blockchain data record by utilizing an SVM algorithm and a random forest model, identifying abnormal network behaviors and generating a behavior analysis report;
and the data fusion module is used for realizing the transmission fusion of the safety data by combining principal component analysis and a depth self-encoder according to the behavior analysis report and outputting the fused safety data.
Firstly, the multi-factor authentication method improves the security of the system in the authentication module, and is more difficult to attack than the traditional single authentication method. And secondly, the key management module ensures the safety life cycle management of the key based on elliptic curve encryption algorithm and blockchain technology, and enhances the safety of the key. The privacy enhancement module protects the data privacy by using homomorphic encryption, differential privacy, secret sharing and zero knowledge proof technology, and improves the privacy protection capability. And the automation script of the audit and compliance module is matched with the strategy, compliance checking and audit log construction are carried out, so that compliance and audit capability are improved. The deep security analysis module automatically discovers security holes and abnormal behaviors by using a convolutional neural network and a decision tree algorithm, and enhances the security analysis capability of the system. The block chain technology module ensures the integrity and traceability of the data and enhances the security of the data. The behavior analysis module carries out deep analysis on the network flow through an SVM algorithm and a random forest model, so that the recognition capability of abnormal network behaviors is improved. Finally, the data fusion module combines principal component analysis and a depth self-encoder to realize fusion of safe data transmission, and improves the adaptability of the system to various types of data. In summary, these benefits together enhance the security, privacy preserving capability, compliance, security analysis capability, and data processing flexibility of the data secure transmission system.
Referring to fig. 2, the identity verification module includes a codon module, a biological feature sub-module, and a hardware token module;
the key management module comprises a key generation sub-module, a key storage sub-module and a key updating sub-module;
the privacy enhancement module comprises a homomorphic encryption sub-module, a differential privacy sub-module and an attribute encryption sub-module;
the audit and compliance block comprises a log record sub-module, a compliance check sub-module and a report generation sub-module;
the deep security analysis module comprises a vulnerability discovery sub-module, an anomaly detection sub-module and a threat prediction sub-module;
the block chain technology module comprises a data uploading sub-module, a data verifying sub-module and a data tracking sub-module;
the behavior analysis module comprises a flow monitoring sub-module, a behavior modeling sub-module and a threat prevention sub-module;
the data fusion module comprises a data encryption sub-module, a feature extraction sub-module and a data aggregation sub-module.
Firstly, the combination of the password sub-module, the biological characteristic sub-module and the hardware token module of the identity verification module provides a multiple identity verification mechanism, and the confirmation and the safety of the system to the user identity are enhanced. The key generation sub-module, the key storage sub-module and the key updating sub-module of the key management module are matched for use, so that safe generation, storage and updating of the key are ensured, and the credibility of the system on key management is improved. The homomorphic encryption sub-module, the differential privacy sub-module and the attribute encryption sub-module in the privacy enhancement module protect the data privacy, and the protection capability of the system on the data privacy is enhanced. The log recording sub-module, the compliance checking sub-module and the report generating sub-module of the auditing and compliance module automate the compliance checking and auditing process, and improve the supervision and recording capacity of the system on compliance. The vulnerability discovery sub-module, the anomaly detection sub-module and the threat prediction sub-module of the deep security analysis module improve the discovery and threat prediction capabilities of security vulnerabilities through deep learning and data analysis technologies. The data uploading sub-module, the data verifying sub-module and the data tracking sub-module of the block chain technology module ensure the integrity, the credibility and the traceability of the data. The flow monitoring sub-module, the behavior modeling sub-module and the threat prevention sub-module of the behavior analysis module improve the recognition and prevention capability of abnormal behaviors and threats by monitoring and modeling the network flow. The data encryption submodule, the feature extraction submodule and the data aggregation submodule of the data fusion module realize safe data transmission fusion, and improve the flexibility and safety of data processing.
Referring to fig. 3, the password submodule adopts a secure hash algorithm to carry out deep encryption processing on a password input by a user to generate an encrypted password report;
the biological characteristic sub-module carries out accurate biological characteristic identification and matching by adopting a convolutional neural network based on the encrypted password report to generate a biological characteristic verification report;
the hardware token submodule is combined with the biological characteristic verification report, and the legitimacy of the hardware token is dynamically verified by using an OTP cryptographic technology to generate an identity verification report.
Firstly, the password submodule uses a secure hash algorithm to carry out deep encryption processing on the password, so as to protect the security of the user password. The biological characteristic sub-module combines the encrypted password report, and adopts a convolutional neural network to realize accurate biological characteristic identification and matching, thereby improving the accuracy and reliability of identity verification. The hardware token submodule utilizes OTP cryptographic technology to dynamically check the token, so that the validity of the token is ensured and the copying or the embezzlement is prevented. The deep encryption of the password ensures the confidentiality of the user password, the accuracy of the biological feature recognition enhances the confirmation of the user identity, and the dynamic verification of the hardware token further increases the security of the identity verification. The improvement schemes improve the protection capability of the system on identity verification and reduce risks of password disclosure, identity impersonation, unauthorized access and the like. In a combined view, the multiple beneficial effects of the identity verification module are that a reliable identity authentication mechanism is provided for the data security transmission system, and the security and the credibility of data transmission are ensured.
Referring to fig. 4, the key generation sub-module establishes an encryption key by using elliptic curve encryption technology based on the authentication report to generate a key generation report;
the key storage submodule generates a report according to the key, and uses the Merker Partrey tree technology to store the key in the blockchain in a lasting manner to generate a key storage record;
a key updating sub-module: and according to the key storage record, the timeliness and the security of the key are ensured through a PFS forward secret technology, and an encryption key token is generated.
First, the key generation sub-module establishes an encryption key by using elliptic curve encryption technology based on the authentication report, and generates a key generation report. Therefore, the generation process of the secret key can be ensured to be based on the reliable information of the verification identity, and the safety of the secret key is enhanced by adopting a safe encryption algorithm.
Next, the key storage submodule generates a key storage record based on the key generation report using the merck patricia tree technique to persist the key in the blockchain. The storage mode guarantees the durability and the non-tamper property of the secret key, and meanwhile, the reliability of secret key storage and the capability of resisting attacks are improved by utilizing the decentralization characteristic of the blockchain.
And finally, the key updating submodule ensures timeliness and safety of the key by utilizing a PFS forward secret technology according to the key storage record and generates an encryption key token. Thus, the key can be updated periodically, the key is prevented from being outdated or maliciously acquired, and the confidentiality of the key is ensured. By an effective key updating mechanism, the system can timely respond to the security requirement and maintain the usability and confidentiality of the key.
Referring to fig. 5, the homomorphic encryption sub-module uses an encryption key token to ensure the privacy of data in the processing process by using FHE homomorphic encryption technology, and generates homomorphic encrypted data;
aiming at homomorphic encryption data, the differential privacy sub-module applies a Laplacian mechanism to carry out differential privacy processing to generate differential privacy data;
and the attribute encryption sub-module implements an ABE attribute-based encryption technology to enhance the privacy of the data according to the differential privacy data, and generates privacy enhanced data.
Firstly, the homomorphic encryption submodule ensures the privacy of data in the processing process by utilizing an encryption key token and FHE homomorphic encryption technology. This technique allows computation in an encrypted state, protecting the privacy of data, while generating homomorphic encrypted data so that the data can be subjected to subsequent processing while maintaining encryption.
Next, the differential privacy submodule applies a laplace mechanism to conduct differential privacy processing on homomorphic encrypted data to generate differential privacy data. By adding noise to the data and limiting anonymization, differential privacy may prevent inferences for individual data to preserve individual privacy. This ensures the usability and analyzability of the data while protecting the privacy of the data.
Finally, the attribute encryption submodule implements an attribute-based encryption (ABE) technology according to the differential privacy data, so that the privacy of the data is enhanced, and privacy enhanced data is generated. The ABE technology allows data to be encrypted and decrypted according to the attribute of a visitor, and only users meeting the requirement of preset attribute can access the data, so that the fine access control and privacy protection capability of the data are improved.
Referring to fig. 6, a logging submodule monitors the operation of privacy enhanced data, and logs by using a system logging protocol to generate an operation log;
the compliance checking sub-module performs policy matching for compliance checking according to the operation log and the regular expression technology to generate a compliance checking report;
the report generation submodule generates audit and compliance reports by integrating and formatting audit and compliance data through the BI Tools business intelligence tool in dependence upon the compliance check report.
First, the logging submodule monitors the operation of the privacy enhanced data and carries out logging, and an operation log is generated by using a system log protocol. By the method, access and operation conditions of data can be tracked, and necessary information basis is provided for follow-up compliance checking and auditing.
Next, a compliance checking sub-module performs policy matching for compliance checking in combination with regular expression technology for the operation log, generating a compliance checking report. By performing policy matching on the operation log, it is possible to check whether there is unauthorized access, violation of privacy specifications, or other violations, ensuring compliance of the system. The generated compliance check report provides the system administrator with detailed information and necessary improvement suggestions for compliance issues.
Finally, the report generation submodule utilizes a business intelligence tool (BI Tools) to integrate and format audit and compliance data to generate audit and compliance reports, depending on the compliance check report. This allows the data in the compliance check report to be integrated and presented to the relevant stakeholder, such as a regulatory agency, an internal management team, or the data owner, in a visual and formatted manner. Audit and compliance reports provide a comprehensive assessment of compliance status, helping decision makers understand and improve the compliance level of the system.
Referring to fig. 7, the vulnerability discovery sub-module performs feature extraction and image classification by using a convolutional neural network based on audit and compliance reports, automatically scans and locates potential security vulnerabilities, and generates a vulnerability location report;
the abnormality detection sub-module applies a decision tree algorithm to perform feature selection and decision rule generation according to the vulnerability positioning report, so as to realize automatic detection of abnormal behaviors and generate an abnormal behavior report;
and the threat prediction sub-module predicts the security threat through the probability model according to the abnormal behavior report and the historical data, and generates a security analysis report.
Firstly, based on audit and compliance reports, the vulnerability discovery sub-module utilizes a convolutional neural network to perform feature extraction and image classification, automatically scans and locates potential security vulnerabilities, and generates a vulnerability positioning report. The automatic vulnerability scanning and positioning can timely discover potential risks in the system, remind a system administrator to take corresponding repair measures, and enhance the safety of the system.
And then, the abnormal detection submodule carries out feature selection and decision rule generation by applying a decision tree algorithm according to the vulnerability positioning report, realizes automatic detection of abnormal behaviors and generates an abnormal behavior report. By analyzing the abnormal behavior of the system, possible malicious activity, unauthorized access, or other abnormal events may be detected, helping to discover and respond to potential security threats early.
And finally, the threat prediction sub-module predicts the security threat through a probability model according to the abnormal behavior report and the historical data, and generates a security analysis report. By analyzing the abnormal behavior and the mode of the historical data, a probability model can be established to predict the possible future security threat, early warning and guidance are provided, and the system manager is helped to take measures in time to alleviate or prevent the potential threat.
Referring to fig. 8, the data uplink sub-module automatically writes the security analysis result into the blockchain using the intelligent contract based on the security analysis report, generating an uplink data record;
the data verification sub-module performs integrity and consistency verification of data by reading the uplink data record and applying the Merkle tree technology to generate a data verification report;
the data tracking sub-module tracks changes and flows of data in the blockchain by adopting a timestamp and a hash function according to the data verification report, and generates a blockchain data record.
First, the data upload sub-module automatically writes security analysis results into the blockchain through the intelligent contract based on the security analysis report, generating the upload data record. This way, the tamper-resistance and transparency of the security analysis results can be ensured, with the results permanently stored in the blockchain.
And then, the data verification sub-module performs integrity and consistency verification of the data by using a Merkle tree technology by reading the uplink data record, and generates a data verification report. By verifying whether hash values in the Merkle tree are consistent, the integrity of the uplink data can be verified, and the data is ensured not to be tampered or damaged. The data validation report provides an explicit proof of trust and integrity of the data.
Finally, the data tracking submodule tracks the change and circulation of the data in the blockchain by adopting a timestamp and a hash function according to the data verification report, and generates a blockchain data record. Each change and stream of data in the blockchain can be tracked through the time stamp and hash function, providing traceability and non-repudiation of the data. Blockchain data records may be used in audits, compliance checks, legal evidence, and other scenarios.
Referring to fig. 9, the traffic monitoring sub-module performs traffic feature classification by using a support vector machine based on the blockchain data record to generate a traffic classification report;
the behavior modeling submodule performs feature screening and classification by using a random forest model according to the flow classification report to generate a behavior modeling result;
and the threat prevention sub-module is used for identifying the network behavior and preventing potential threats according to the behavior modeling result by applying a rule engine and threshold setting, and generating a behavior analysis report.
Firstly, by accurately classifying network traffic and modeling network behavior, the system can improve network security and effectively identify potential threats and abnormal behaviors, thereby greatly reducing the risk of network attack. Secondly, the system can reduce the security vulnerability exploitation, and the potential security vulnerability exploitation is timely prevented by using a rule engine and threshold setting in the threat prevention sub-module, so that the overall security of the network is improved. In addition, the system has the capabilities of real-time monitoring and behavior analysis, can quickly respond to threat events, quickly discover and cope with network threats, and reduces potential safety risks. Finally, the system provides detailed flow classification reports, behavior modeling results and behavior analysis reports for network administrators, visual indexes and decision support are provided, the administrators are helped to make intelligent decisions, and network security is enhanced.
Referring to fig. 10, the data encryption sub-module performs secure transmission encryption processing on data based on a behavior analysis report by adopting a symmetric or asymmetric encryption algorithm to generate encrypted data;
the feature extraction submodule applies principal component analysis to carry out high-dimensional data dimension reduction and feature extraction based on the encrypted data to generate a feature data set;
and the data aggregation submodule encodes and decodes the data features by utilizing the depth self-encoder according to the feature data set to generate fused safety data.
Firstly, the data encryption sub-module adopts a symmetric or asymmetric encryption algorithm to carry out safe transmission encryption processing on data based on a behavior analysis report, and generates encrypted data. The encryption processing can protect confidentiality and privacy of data and reduce risks of malicious theft or peeping of the data in the transmission process.
Next, the feature extraction submodule performs high-dimensional data dimension reduction and feature extraction based on the encrypted data by applying technologies such as principal component analysis and the like, and generates a feature data set. By dimension reduction and feature extraction, the dimension and redundancy of the data can be reduced, important features of the data can be better captured, and more efficient data representation is provided for subsequent data processing and analysis.
And finally, the data aggregation submodule encodes and decodes the data features by utilizing the technologies such as a depth self-encoder and the like according to the feature data set to generate fused safety data. Through the encoding and decoding processes, multiple feature data sets can be fused and reconstructed to generate a safer and privacy-protected data form. Such secure data may be used for further data analysis, modeling or sharing while reducing the risk of leakage of sensitive information.
Working principle:
and an identity verification module: and carrying out identity authentication by adopting a multi-factor authentication method. First, an encrypted password report is generated by encrypting a password input by a user using a secure hash algorithm. And secondly, the biological characteristic sub-module uses a convolutional neural network to carry out biological characteristic recognition and matching, and generates a biological characteristic verification report. Finally, the hardware token module combines the biological characteristic verification report to dynamically verify the validity of the hardware token by using a one-time password technology, and an identity verification report is generated.
A key management module: and according to the result of the generation of the authentication report, adopting elliptic curve cryptography and blockchain technology to manage the safety life cycle of the secret key. The key generation sub-module establishes an encryption key using elliptic curve cryptography and generates a key generation report. The key storage sub-module persists the key in the blockchain using the merck patricia tree technique and generates a key storage record. The key updating sub-module uses a PFS forward secret technology to ensure the timeliness and the security of the key and generates an encryption key token.
Privacy enhancement module: and the encryption key token is utilized, and homomorphic encryption and differential privacy technologies are adopted to ensure the privacy of the data. And the homomorphic encryption submodule encrypts the data by using an FHE homomorphic encryption technology to generate homomorphic encrypted data. And the differential privacy sub-module performs differential privacy processing on homomorphic encrypted data by using a Laplacian mechanism to generate differential privacy data. The attribute encryption submodule enhances the privacy of the data by using an ABE attribute-based encryption technology and generates privacy enhanced data.
Audit and aggregate block: compliance checking is performed by automated script matching with policies based on privacy enhanced data. The log recording submodule monitors the operation of the privacy enhanced data, and uses a system log protocol to carry out log recording to generate an operation log. The compliance checking sub-module performs policy matching by using a regular expression technology, performs compliance checking, and generates a compliance checking report. The report generation sub-module uses a business intelligence tool to integrate and format audit and compliance data to generate audit and compliance reports.
Depth safety analysis module: and automatically discovering security holes and detecting abnormal behaviors by utilizing audit and compliance reports and adopting a convolutional neural network and a decision tree algorithm. And the vulnerability discovery sub-module uses a convolutional neural network to perform feature extraction and image classification, automatically scans and locates potential security vulnerabilities, and generates a vulnerability location report. And the anomaly detection submodule carries out feature selection and decision rule generation by using a decision tree algorithm according to the vulnerability positioning report, realizes automatic detection of the anomaly, and generates an anomaly report. The threat prediction sub-module predicts security threats using a probabilistic model based on the abnormal behavior report and the historical data and generates a security analysis report.
The block chain technology module: and automatically writing the security analysis result into the blockchain by using an intelligent contract and Merkle tree technology according to the security analysis report to generate a uplink data record. And the data verification sub-module performs integrity and consistency verification of the data by reading the uplink data record and using a Merkle tree technology, and generates a data verification report. The data tracking submodule tracks the change and circulation of the data in the blockchain by using the time stamp and the hash function, and generates a blockchain data record.
Behavior analysis module: based on the blockchain data record, the SVM algorithm and the random forest model are used for carrying out deep analysis on the network traffic, and abnormal network behaviors are identified. And the flow monitoring sub-module uses a support vector machine to conduct flow characteristic classification and generates a flow classification report. And the behavior modeling submodule performs feature screening and classification by utilizing a random forest model according to the flow classification report to generate a behavior modeling result. And the threat prevention sub-module is used for identifying the network behavior and preventing potential threats according to the behavior modeling result by applying a rule engine and threshold setting, and generating a behavior analysis report.
And a data fusion module: based on the behavior analysis report, the data is subjected to safe transmission encryption processing by using an encryption algorithm, and encrypted data is generated. The feature extraction submodule applies principal component analysis to carry out high-dimensional data dimension reduction and feature extraction based on the encrypted data to generate a feature data set. And the data aggregation submodule encodes and decodes the data features by utilizing the depth self-encoder according to the feature data set to generate fused safety data.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.

Claims (10)

1. A data security transmission system, characterized in that: the data security transmission system comprises an identity verification module, a key management module, a privacy enhancement module, an audit and compliance module, a deep security analysis module, a blockchain technology module, a behavior analysis module and a data fusion module;
the identity verification module adopts a multi-factor verification method, performs identity authentication by combining a password, a neural network model of biological feature recognition and a dynamic matching technology of a hardware token through an advanced hash algorithm, and generates an identity verification report;
the key management module uses an elliptic curve-based encryption algorithm and a blockchain technology according to the identity verification report to ensure the safe life cycle management of the key and produce an encrypted key token;
the privacy enhancement module utilizes an encryption key token, combines secret sharing and zero knowledge proof, and adopts homomorphic encryption and differential privacy technology to ensure data privacy to obtain privacy enhanced data;
the audit and compliance block is matched with the policy based on privacy enhancement data by adopting an automatic script, performs compliance checking, builds an audit log and generates audit and compliance reports;
the deep security analysis module automatically discovers security holes by utilizing audit and compliance reports and applying a convolutional neural network and a decision tree algorithm, detects abnormal behaviors and compiles security analysis reports;
the block chain technology module forms a block chain data record by using an intelligent contract and Merkle tree technology according to the security analysis report;
the behavior analysis module is used for carrying out deep analysis on network traffic by utilizing an SVM algorithm and a random forest model based on the blockchain data record, identifying abnormal network behaviors and generating a behavior analysis report;
and the data fusion module is used for realizing the transmission fusion of the safety data by combining the principal component analysis and the depth self-encoder according to the behavior analysis report and outputting the fused safety data.
2. The data security transmission system according to claim 1, wherein: the identity verification module comprises a code sub-module, a biological characteristic sub-module and a hardware token module;
the key management module comprises a key generation sub-module, a key storage sub-module and a key updating sub-module;
the privacy enhancement module comprises a homomorphic encryption sub-module, a differential privacy sub-module and an attribute encryption sub-module;
the audit and compliance block comprises a log record sub-module, a compliance check sub-module and a report generation sub-module;
the deep security analysis module comprises a vulnerability discovery sub-module, an anomaly detection sub-module and a threat prediction sub-module;
the block chain technology module comprises a data uploading sub-module, a data verifying sub-module and a data tracking sub-module;
the behavior analysis module comprises a flow monitoring sub-module, a behavior modeling sub-module and a threat prevention sub-module;
the data fusion module comprises a data encryption sub-module, a feature extraction sub-module and a data aggregation sub-module.
3. The data security transmission system according to claim 2, wherein: the password submodule adopts a secure hash algorithm to carry out deep encryption processing on a password input by a user, and generates an encrypted password report;
the biological characteristic sub-module carries out accurate biological characteristic identification and matching by adopting a convolutional neural network based on the encrypted password report to generate a biological characteristic verification report;
the hardware token module combines the biological characteristic verification report, dynamically verifies the validity of the hardware token by using an OTP cryptographic technology, and generates an identity verification report.
4. The data security transmission system according to claim 2, wherein: the key generation sub-module establishes an encryption key by adopting elliptic curve encryption technology based on the identity verification report to generate a key generation report;
the key storage submodule is used for storing the key in the blockchain in a lasting mode by using the Merker Partrey tree technology according to the key generation report to generate a key storage record;
the key update sub-module: and according to the key storage record, the timeliness and the security of the key are ensured through a PFS forward secret technology, and an encryption key token is generated.
5. The data security transmission system according to claim 2, wherein: the homomorphic encryption sub-module uses an encryption key token, and utilizes FHE homomorphic encryption technology to ensure privacy in the data processing process, so as to generate homomorphic encryption data;
the differential privacy submodule applies a Laplace mechanism to conduct differential privacy processing aiming at homomorphic encrypted data to generate differential privacy data;
and the attribute encryption submodule implements an ABE attribute-based encryption technology to enhance the privacy of the data according to the differential privacy data, and generates privacy enhanced data.
6. The data security transmission system according to claim 2, wherein: the log recording submodule monitors the operation of the privacy enhanced data, and uses a system log protocol to carry out log recording to generate an operation log;
the compliance checking sub-module performs policy matching for compliance checking by combining a regular expression technology aiming at the operation log to generate a compliance checking report;
the report generation submodule generates audit and compliance reports by integrating and formatting audit and compliance data with BI Tools business intelligence Tools in dependence upon the compliance check report.
7. The data security transmission system according to claim 2, wherein: the vulnerability discovery submodule adopts a convolutional neural network to perform feature extraction and image classification based on audit and compliance reports, automatically scans and locates potential security vulnerabilities, and generates a vulnerability location report;
the abnormality detection sub-module performs feature selection and decision rule generation by applying a decision tree algorithm according to the vulnerability positioning report, realizes automatic detection of abnormal behaviors and generates an abnormal behavior report;
and the threat prediction sub-module predicts the security threat through a probability model according to the abnormal behavior report and the historical data, and generates a security analysis report.
8. The data security transmission system according to claim 2, wherein: the data uplink sub-module automatically writes the security analysis result into the blockchain by using an intelligent contract based on the security analysis report to generate an uplink data record;
the data verification sub-module performs integrity and consistency verification of data by reading the uplink data record and applying the Merkle tree technology to generate a data verification report;
the data tracking submodule tracks the change and circulation of data in the blockchain by adopting a timestamp and a hash function according to the data verification report, and generates a blockchain data record.
9. The data security transmission system according to claim 2, wherein: the flow monitoring submodule carries out flow characteristic classification by adopting a support vector machine based on the blockchain data record to generate a flow classification report;
the behavior modeling submodule performs feature screening and classification by utilizing a random forest model according to the flow classification report to generate a behavior modeling result;
and the threat prevention sub-module is used for identifying the network behavior and preventing potential threats according to the behavior modeling result by applying a rule engine and threshold setting, and generating a behavior analysis report.
10. The data security transmission system according to claim 2, wherein: the data encryption sub-module carries out safe transmission encryption processing on data by adopting a symmetrical or asymmetrical encryption algorithm based on the behavior analysis report to generate encrypted data;
the feature extraction submodule applies principal component analysis to carry out high-dimensional data dimension reduction and feature extraction based on the encrypted data to generate a feature data set;
and the data aggregation submodule encodes and decodes the data characteristics by utilizing a depth self-encoder according to the characteristic data set to generate fused safety data.
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