Transaction data security monitoring system and method for e-commerce

Abstract

The present invention relates to the technical field of data security supervision, and in particular to a transaction data security supervision system and method for e-commerce. The method comprises the following steps: obtaining original e-commerce transaction data; performing data segment complexity calculation on the original e-commerce transaction data to generate the complexity of e-commerce transaction data segments; performing complexity integration on the complexity of e-commerce transaction data segments to generate an e-commerce transaction encryption entropy index; performing hash conversion on the e-commerce transaction encryption entropy index to generate e-commerce transaction encryption entropy hash data; performing data security encryption on standard e-commerce transaction data according to the e-commerce transaction encryption entropy hash data to generate e-commerce transaction encryption data. The present invention improves the security of e-commerce transaction supervision by utilizing data complexity calculation, multi-level access control, and abnormal traffic monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of data security supervision, and in particular to a transaction data security supervision system and method for e-commerce. Background Art

[0002] With the rise of e-commerce, traditional encryption technology has gradually exposed its inability to cope with large-scale data transactions and network attacks. To meet this challenge, modern cryptography technology has emerged. Symmetric encryption algorithms such as AES and DES provide a higher level of security, while hash functions such as SHA-256 are used to ensure data integrity. In addition, technologies such as digital signatures and digital certificates are also widely used to verify the authenticity and credibility of data. With the rapid development of cloud computing and big data technology, the storage and processing methods of transaction data have also changed. The emergence of cloud security technology provides e-commerce with more flexible and reliable data storage and management solutions. For example, technologies such as data encryption, access control and identity authentication have become important means to protect the security of transaction data. In recent years, the development of artificial intelligence and machine learning technologies has brought new possibilities for transaction data security supervision. Anomaly detection and behavior analysis technologies based on machine learning can help identify and prevent potential security threats, thereby improving the security and credibility of transaction data. However, the current traditional access control method simply performs one-time permission control on the entire data set. At the same time, traditional security monitoring methods can only passively monitor data traffic and respond after anomalies are found, which leads to low data supervision capabilities and poor security. Summary of the invention

[0003] Based on this, it is necessary to provide a transaction data security supervision system and method for e-commerce to solve at least one of the above technical problems.

[0004] To achieve the above object, a transaction data security supervision method for e-commerce is provided, the method comprising the following steps:

[0005] Step S1: obtaining original e-commerce transaction data; performing data segment complexity calculation on the original e-commerce transaction data to generate e-commerce transaction data segment complexity; performing complexity integration on the e-commerce transaction data segment complexity to generate an e-commerce transaction encryption entropy index; performing hash conversion on the e-commerce transaction encryption entropy index to generate e-commerce transaction encryption entropy hash data;

[0006] Step S2: Perform data security encryption on standard e-commerce transaction data according to the e-commerce transaction encryption entropy hash data to generate e-commerce transaction encrypted data; perform data transmission flow analysis on the e-commerce transaction encrypted data to generate an e-commerce transaction data flow graph; construct a multi-level access control strategy according to the e-commerce transaction data flow graph to generate an e-commerce transaction multi-level access control strategy;

[0007] Step S3: Based on the multi-level access control strategy of e-commerce transactions, access traffic is monitored on the e-commerce transaction data flow graph to obtain hierarchical access traffic monitoring data; traffic is predicted on the hierarchical access traffic monitoring data to generate hierarchical access traffic prediction data; access period analysis is performed on the hierarchical access traffic prediction data to generate normal traffic access period and abnormal traffic access period; abnormal traffic response is performed on the abnormal traffic access period to generate abnormal traffic warning data;

[0008] Step S4: elastically distribute and store the encrypted data of e-commerce transactions based on normal traffic access periods to generate e-commerce transaction storage data; perform backup coverage detection on the e-commerce transaction storage data at preset time intervals to generate electronic transaction backup coverage detection data; conduct e-commerce transaction security assessments based on abnormal traffic warning data and electronic transaction backup coverage detection data to perform e-commerce transaction data security supervision operations.

[0009] The present invention can establish a basis for data processing by acquiring original transaction data. By calculating the complexity of data fragments, the security and difficulty of cracking data can be evaluated. Generating an encryption entropy index can quantify the encryption degree of data and provide guidance for subsequent encryption processing. By encrypting standard e-commerce transaction data, the confidentiality of data can be protected and unauthorized access and leakage can be prevented. By analyzing the transmission traffic of encrypted data, potential abnormal behavior and security threats can be detected. Establishing a multi-level access control strategy can ensure that data is only accessed by authorized personnel and improve the security of data. By monitoring the data flow graph, access patterns and behaviors can be tracked in real time and abnormal situations can be discovered in time. By predicting and analyzing the monitoring data, future traffic changes can be predicted to provide a basis for resource allocation and capacity planning. According to the analysis results of the abnormal traffic access period, corresponding measures can be taken in time to protect the security of data, and warning data can be generated to remind relevant personnel. By performing elastic distributed storage on encrypted data, the availability and fault tolerance of data can be improved and data loss can be prevented. Regular backup and overwriting detection of stored data can ensure the integrity and recoverability of data. By combining abnormal traffic warning data and backup coverage detection data for security assessment, potential security risks and loopholes can be discovered in a timely manner, and corresponding regulatory measures can be taken. Therefore, the present invention improves the security of e-commerce transaction supervision by utilizing data complexity calculation, multi-level access control, and abnormal traffic monitoring.

[0010] In this specification, a transaction data security supervision system for e-commerce is provided, which is used to execute the above-mentioned transaction data security supervision method for e-commerce. The transaction data security supervision system for e-commerce includes:

[0011] The transaction complexity calculation module is used to obtain the original e-commerce transaction data; perform data segment complexity calculation on the original e-commerce transaction data to generate the complexity of the e-commerce transaction data segment; perform complexity integration on the complexity of the e-commerce transaction data segment to generate the e-commerce transaction encryption entropy index; perform hash conversion on the e-commerce transaction encryption entropy index to generate the e-commerce transaction encryption entropy hash data;

[0012] The multi-layer access control module is used to perform data security encryption on standard e-commerce transaction data according to the e-commerce transaction encryption entropy hash data to generate e-commerce transaction encrypted data; perform data transmission flow analysis on the e-commerce transaction encrypted data to generate an e-commerce transaction data flow graph; construct a multi-layer access control strategy according to the e-commerce transaction data flow graph to generate a multi-layer access control strategy for e-commerce transactions;

[0013] The traffic monitoring module is used to monitor the access traffic of the e-commerce transaction data traffic graph based on the multi-level access control strategy of the e-commerce transaction, and obtain the hierarchical access traffic monitoring data; perform traffic prediction on the hierarchical access traffic monitoring data to generate hierarchical access traffic prediction data; perform access period analysis on the hierarchical access traffic prediction data to generate normal traffic access period and abnormal traffic access period; perform abnormal traffic response on the abnormal traffic access period, thereby generating abnormal traffic warning data;

[0014] The transaction security assessment module is used to perform elastic distributed storage of e-commerce transaction encrypted data based on normal traffic access periods to generate e-commerce transaction storage data; perform backup coverage detection on e-commerce transaction storage data according to preset time intervals to generate electronic transaction backup coverage detection data; perform e-commerce transaction security assessment through abnormal traffic warning data and electronic transaction backup coverage detection data to perform e-commerce transaction data security supervision operations.

[0015] The beneficial effect of the present invention is that by performing complexity calculation, encryption and multi-level access control strategy construction on e-commerce transaction data, the security of data is effectively improved, and the transaction data is protected from unauthorized access or tampering. Through traffic monitoring, prediction and abnormal traffic response, abnormal traffic behavior can be discovered in time, and corresponding measures can be taken to ensure the normal operation and security of e-commerce transaction data. Through elastic distributed storage of backup data and regular backup coverage detection, the integrity and recoverability of transaction data are guaranteed, ensuring that data can be restored in time when it is lost or damaged. Security assessment and supervision are performed through abnormal traffic warning data and backup coverage detection data, the security status of transaction data can be fully understood, security issues can be discovered and resolved in time, and the level of data security management can be improved. Therefore, the present invention improves the security of e-commerce transaction supervision by utilizing data complexity calculation, multi-level access control, and abnormal traffic monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the steps of a transaction data security supervision method for e-commerce;

[0017] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0018] Figure 3 for Figure 2 Detailed implementation steps of step S24;

[0019] Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0020] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0023] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0024] To achieve this, please refer to Figures 1 to 4 , a transaction data security supervision method for e-commerce, the method comprising the following steps:

[0025] Step S1: obtaining original e-commerce transaction data; performing data segment complexity calculation on the original e-commerce transaction data to generate e-commerce transaction data segment complexity; performing complexity integration on the e-commerce transaction data segment complexity to generate an e-commerce transaction encryption entropy index; performing hash conversion on the e-commerce transaction encryption entropy index to generate e-commerce transaction encryption entropy hash data;

[0026] Step S2: Perform data security encryption on standard e-commerce transaction data according to the e-commerce transaction encryption entropy hash data to generate e-commerce transaction encrypted data; perform data transmission flow analysis on the e-commerce transaction encrypted data to generate an e-commerce transaction data flow graph; construct a multi-level access control strategy according to the e-commerce transaction data flow graph to generate an e-commerce transaction multi-level access control strategy;

[0027] Step S3: Based on the multi-level access control strategy of e-commerce transactions, access traffic is monitored on the e-commerce transaction data flow graph to obtain hierarchical access traffic monitoring data; traffic is predicted on the hierarchical access traffic monitoring data to generate hierarchical access traffic prediction data; access period analysis is performed on the hierarchical access traffic prediction data to generate normal traffic access period and abnormal traffic access period; abnormal traffic response is performed on the abnormal traffic access period to generate abnormal traffic warning data;

[0028] Step S4: elastically distribute and store the encrypted data of e-commerce transactions based on normal traffic access periods to generate e-commerce transaction storage data; perform backup coverage detection on the e-commerce transaction storage data at preset time intervals to generate electronic transaction backup coverage detection data; conduct e-commerce transaction security assessments based on abnormal traffic warning data and electronic transaction backup coverage detection data to perform e-commerce transaction data security supervision operations.

[0029] The present invention can establish a basis for data processing by acquiring original transaction data. By calculating the complexity of data fragments, the security and difficulty of cracking data can be evaluated. Generating an encryption entropy index can quantify the encryption degree of data and provide guidance for subsequent encryption processing. By encrypting standard e-commerce transaction data, the confidentiality of data can be protected and unauthorized access and leakage can be prevented. By analyzing the transmission traffic of encrypted data, potential abnormal behavior and security threats can be detected. Establishing a multi-level access control strategy can ensure that data is only accessed by authorized personnel and improve the security of data. By monitoring the data flow graph, access patterns and behaviors can be tracked in real time and abnormal situations can be discovered in time. By predicting and analyzing the monitoring data, future traffic changes can be predicted to provide a basis for resource allocation and capacity planning. According to the analysis results of the abnormal traffic access period, corresponding measures can be taken in time to protect the security of data, and warning data can be generated to remind relevant personnel. By performing elastic distributed storage on encrypted data, the availability and fault tolerance of data can be improved and data loss can be prevented. Regular backup and overwriting detection of stored data can ensure the integrity and recoverability of data. By combining abnormal traffic warning data and backup coverage detection data for security assessment, potential security risks and loopholes can be discovered in a timely manner, and corresponding regulatory measures can be taken. Therefore, the present invention improves the security of e-commerce transaction supervision by utilizing data complexity calculation, multi-level access control, and abnormal traffic monitoring.

[0030] In the embodiment of the present invention, reference Figure 1The above is a schematic diagram of the steps of a transaction data security supervision method for e-commerce of the present invention. In this example, the transaction data security supervision method for e-commerce includes the following steps:

[0031] Step S1: obtaining original e-commerce transaction data; performing data segment complexity calculation on the original e-commerce transaction data to generate e-commerce transaction data segment complexity; performing complexity integration on the e-commerce transaction data segment complexity to generate an e-commerce transaction encryption entropy index; performing hash conversion on the e-commerce transaction encryption entropy index to generate e-commerce transaction encryption entropy hash data;

[0032] In an embodiment of the present invention, the original transaction data is obtained from an e-commerce system or other data sources, including information such as transaction time, transaction amount, and transaction content. The original transaction data is segmented and divided into multiple data fragments. The complexity of each data fragment is calculated, and various complexity measurement methods are specifically adopted, such as information entropy, Kolmogorov complexity, etc. The complexity of each data fragment is integrated, specifically by averaging, weighted average, etc., to obtain the overall transaction data complexity. After integrating the complexity of the transaction data fragments, an encryption entropy index is generated to measure the overall complexity and randomness of the transaction data. The encryption entropy index is hashed and converted into a hash value of a fixed length for subsequent data security encryption and processing. Common hash algorithms such as MD5, SHA-256, etc. are specifically used for conversion.

[0033] Step S2: Perform data security encryption on standard e-commerce transaction data according to the e-commerce transaction encryption entropy hash data to generate e-commerce transaction encrypted data; perform data transmission flow analysis on the e-commerce transaction encrypted data to generate an e-commerce transaction data flow graph; construct a multi-level access control strategy according to the e-commerce transaction data flow graph to generate an e-commerce transaction multi-level access control strategy;

[0034] In an embodiment of the present invention, standard e-commerce transaction data is encrypted by using e-commerce transaction encryption entropy hash data as a key or parameter of an encryption algorithm. Common encryption algorithms such as AES, RSA, etc. can be used to ensure the confidentiality and security of data. The transmission flow of e-commerce transaction encrypted data is monitored, and information such as the frequency, size, source, and destination of data transmission is recorded. This can be achieved through network monitoring tools, data packet analyzers, etc. Based on the results of data transmission flow analysis, an e-commerce transaction data flow diagram is drawn. The diagram reflects the flow of data in the network, including information such as the path and amount of data transmission. According to the e-commerce transaction data flow diagram, a multi-level access control strategy is constructed to protect the security and privacy of data. The strategy may include multiple levels such as access control at the network level and access control at the application level. At the network level, the transmission scope and permissions of data can be limited by technologies such as firewalls and access control lists. At the application level, identity authentication, access permission management, etc. can be used to ensure that only authorized users can access data.

[0035] Step S3: Based on the multi-level access control strategy of e-commerce transactions, access traffic is monitored on the e-commerce transaction data flow graph to obtain hierarchical access traffic monitoring data; traffic is predicted on the hierarchical access traffic monitoring data to generate hierarchical access traffic prediction data; access period analysis is performed on the hierarchical access traffic prediction data to generate normal traffic access period and abnormal traffic access period; abnormal traffic response is performed on the abnormal traffic access period to generate abnormal traffic warning data;

[0036] In the embodiment of the present invention, the data flow graph of e-commerce transactions is monitored in real time based on the multi-level access control strategy of e-commerce transactions, which is specifically implemented through a network monitoring system, a log recording tool, etc. The source, destination, frequency, size and other information of the data flow are monitored so as to timely discover abnormal flow behavior. Based on the monitoring data, the flow in the future period is predicted using a prediction model. The prediction can be performed using time series analysis, machine learning and other methods to obtain predicted hierarchical access flow data. The predicted hierarchical access flow data is analyzed to determine the normal flow access period and the abnormal flow access period. This can be achieved by comparing the difference between the actual flow and the predicted flow. The normal flow access period refers to the time period when the predicted flow and the actual flow are consistent, and the abnormal flow access period refers to the time period when the predicted flow and the actual flow are significantly different. Respond to the abnormal flow access period and take corresponding security measures, including increasing security monitoring, restricting access rights, rejecting abnormal flow requests and other measures. The purpose of the response is to respond to abnormal flow behavior in a timely manner and ensure the safe and stable operation of the e-commerce transaction system.

[0037] Step S4: elastically distribute and store the encrypted data of e-commerce transactions based on normal traffic access periods to generate e-commerce transaction storage data; perform backup coverage detection on the e-commerce transaction storage data at preset time intervals to generate electronic transaction backup coverage detection data; conduct e-commerce transaction security assessments based on abnormal traffic warning data and electronic transaction backup coverage detection data to perform e-commerce transaction data security supervision operations.

[0038] In an embodiment of the present invention, the encrypted data of e-commerce transactions is stored in an elastic distributed storage system according to the normal traffic access period, which is specifically based on cloud storage services such as AWS S3, Google Cloud Storage, etc., or a private distributed storage system such as Hadoop HDFS, etc. The elastic distributed storage system has high reliability and scalability, and can ensure the security and availability of data. The e-commerce transaction storage data is backed up regularly according to the preset time interval. The backup is specifically a full backup or an incremental backup to ensure the integrity and recoverability of the data. The backup data is overwritten to ensure the update and integrity of the backup data. This is specifically achieved by comparing the differences between the backup data and the original data. Combined with the abnormal traffic warning data and the backup overwrite detection data, the security assessment of the e-commerce transaction is performed, which is specifically based on the rule-based assessment. The purpose of the assessment is to detect potential security risks and problems, take corresponding security measures in a timely manner, and ensure the security of e-commerce transaction data.

[0039] Preferably, step S1 comprises the following steps:

[0040] Step S11: Obtaining original e-commerce transaction data;

[0041] Step S12: preprocessing the original e-commerce transaction data to generate standard e-commerce transaction data, wherein the data preprocessing includes data denoising, data missing value filling, data normalization and data standardization;

[0042] Step S13: segmenting the standard e-commerce transaction data into data segments to obtain e-commerce transaction data segments; transmitting the e-commerce transaction data segments to the distributed server nodes to perform data complexity calculation to generate the complexity of the e-commerce transaction data segments;

[0043] Step S14: performing complexity integration on the complexity of the e-commerce transaction data fragments to generate an e-commerce transaction encryption entropy index; performing hash conversion on the e-commerce transaction encryption entropy index to generate e-commerce transaction encryption entropy hash data.

[0044] The present invention can improve data security and prevent data leakage and unauthorized access by performing encrypted entropy processing on transaction data. By calculating data complexity, the characteristics and structure of e-commerce transaction data can be better understood, which helps to discover abnormal transactions or trends. Hash conversion can convert raw data into an irreversible string, which helps to protect data privacy and maintain data security even during transmission or storage. Using distributed servers for data complexity calculation can improve processing speed and efficiency, especially when processing large-scale data. Standardization and normalization operations in data preprocessing can improve data consistency and comparability, which is helpful for subsequent data analysis and mining.

[0045] In an embodiment of the present invention, raw transaction data is obtained from an e-commerce platform, a database or other data source. To ensure the integrity and accuracy of data acquisition, the frequency of data collection and the way of data update need to be considered. Various techniques (such as smoothing, filtering, etc.) are used to remove noise in the data to improve data quality. Appropriate filling strategies are adopted for missing values, specifically, simple mean filling, median filling, or more complex interpolation methods are adopted. The data is scaled to a uniform range or distribution to eliminate the dimensional effects between different features. Common methods include minimum-maximum scaling and Z-score standardization. Standard data is divided into different fragments according to certain rules, and can be divided according to time, transaction type or other characteristics. Ensure the integrity and continuity of data fragments, while taking into account the distribution of data. On the distributed server node, the complexity of each data fragment is calculated, and various complexity indicators such as information entropy, model complexity, etc. can be used. The complexity of each data fragment is integrated into an overall indicator, which can be a simple weighted average or other complex integration methods. The complexity indicator is hashed, and a common hash algorithm such as MD5, SHA, etc. is specifically used. Ensure the security and irreversibility of the hash algorithm to protect the security and integrity of encrypted entropy data.

[0046] Preferably, step S2 comprises the following steps:

[0047] Step S21: performing data security encryption on standard e-commerce transaction data according to the e-commerce transaction encryption entropy hash data to generate e-commerce transaction encrypted data;

[0048] Step S22: Based on the IFP technology, the transaction data flow is monitored on the e-commerce transaction encrypted data to obtain the e-commerce transaction data flow; the data transmission flow is analyzed on the e-commerce transaction data flow to generate an e-commerce transaction data flow graph;

[0049] Step S23: performing user access pattern recognition on the e-commerce transaction data flow graph to generate user access pattern data; performing user access behavior analysis on the user access pattern data to generate user access behavior data;

[0050] Step S24: construct a multi-level access control strategy based on the user access pattern data and the user access behavior data to generate a multi-level access control strategy for e-commerce transactions.

[0051] The present invention can effectively protect the privacy and security of data and prevent unauthorized access and theft by encrypting standard e-commerce transaction data. By monitoring the flow of encrypted data based on IFP technology, the data flow can be understood in real time, and abnormal or suspicious transaction behaviors can be discovered in time. Analyzing the e-commerce transaction data flow can reveal transaction patterns, trends, and anomalies, which helps optimize business processes and decision-making. By identifying user access patterns and analyzing user behavior, users' preferences and behavioral habits can be deeply understood, providing a basis for personalized recommendations and marketing. By building a multi-level access control strategy based on user access patterns and behavioral data, different levels of security measures can be implemented according to the user's identity, authority, and behavior, thereby improving the security and reliability of the system.

[0052] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0053] Step S21: performing data security encryption on standard e-commerce transaction data according to the e-commerce transaction encryption entropy hash data to generate e-commerce transaction encrypted data;

[0054] In an embodiment of the present invention, the e-commerce transaction encryption entropy hash data generated in step S14 is obtained, which is a hash conversion result of the complexity of the transaction data. The standard e-commerce transaction data is encrypted using an encryption algorithm. Common encryption algorithms include symmetric encryption algorithms (such as AES, DES) and asymmetric encryption algorithms (such as RSA, ECC). Ensure the security and management of keys during the encryption process. Symmetric encryption algorithms need to ensure the secure transmission and storage of keys, while asymmetric encryption algorithms need to manage the security of public and private keys. Set the parameters of the encryption algorithm, including key length, padding mode, encryption mode, etc. The selection of these parameters needs to be based on security requirements and performance considerations. Apply the selected encryption algorithm and parameters to the standard e-commerce transaction data for encryption operations. Ensure that the encrypted data can maintain the integrity, confidentiality and availability of the data, and save the encrypted data as e-commerce transaction encrypted data.

[0055] Step S22: Based on the IFP technology, the transaction data flow is monitored on the e-commerce transaction encrypted data to obtain the e-commerce transaction data flow; the data transmission flow is analyzed on the e-commerce transaction data flow to generate an e-commerce transaction data flow graph;

[0056] In an embodiment of the present invention, the transmission flow of encrypted data of e-commerce transactions is monitored by using IFP technology. This involves deploying IFP software or hardware on network devices (such as routers and switches) and configuring appropriate filtering rules to capture target data flows, wherein IFP is a technology used for network traffic analysis that can capture and analyze data packets and extract useful information therein. The IFP device is configured to capture encrypted data traffic passing through the network, specifically by setting appropriate filtering conditions to capture only data flows related to e-commerce transactions to reduce unnecessary data processing. The captured data flows are analyzed and useful information is extracted therefrom, which involves parsing and filtering data packets to identify and extract data related to e-commerce transactions. Based on the analyzed data, an e-commerce transaction data flow diagram is generated, specifically in the form of a timing diagram, a traffic distribution diagram, etc., to intuitively display the data flow situation of e-commerce transactions.

[0057] Step S23: performing user access pattern recognition on the e-commerce transaction data flow graph to generate user access pattern data; performing user access behavior analysis on the user access pattern data to generate user access behavior data;

[0058] In an embodiment of the present invention, the e-commerce transaction data flow graph generated in step S22 is analyzed, including analysis of the time distribution, source distribution, destination distribution, etc. of the data flow. Based on the analysis of the data flow graph, the access patterns of different users are identified, including characteristics of the user's access frequency, access period, access source, access destination, etc. Based on the identified user access pattern, user access pattern data is generated, specifically a structured data table that records the access pattern characteristics of each user, or it can be an unstructured data form, such as a description or label of the user access pattern. Based on the user access pattern data, user access behavior analysis is performed, including analysis of the frequency, duration, page browsing order, transaction behavior, etc. of the user behavior. Based on the user access behavior analysis results, user access behavior data is generated, specifically a structured data table that records the specific access behavior of each user, or it can be an unstructured data form, such as a description or label of the user behavior.

[0059] Step S24: construct a multi-level access control strategy based on the user access pattern data and the user access behavior data to generate a multi-level access control strategy for e-commerce transactions.

[0060] In the embodiment of the present invention, a multi-level access control strategy is designed according to the results of the user access mode and behavior data analysis. Considering the access characteristics and needs of different user groups, a multi-level access control strategy is specifically designed. An identity authentication layer is designed to ensure that the user's identity is authentic and legal, including identity authentication using username and password, verification code, two-factor authentication, etc. A permission management layer is designed to control the user's access rights to system resources according to the user's identity and access needs, specifically role-based access control (RBAC), attribute-based access control (ABAC), etc. A behavior analysis layer is designed to monitor and analyze the user's real-time behavior. Based on the user's behavior pattern and historical data, abnormal behavior is detected and prevented. An access audit layer is designed to record and audit the user's access behavior. Ensure that the system can track and record the user's operations so as to investigate and trace the source when a security incident occurs. Integrate and optimize the various designed access control strategies to ensure collaborative work and unified management between the various levels. Implement the designed access control strategy into the system, and test and verify it. Ensure that the access control strategy can effectively protect the system security without affecting the normal user experience.

[0061] Preferably, step S23 includes the following steps:

[0062] Perform time series pattern recognition on the e-commerce transaction data flow graph to generate user access time series data, where the user access time series data includes user access cycle data and user access trend data;

[0063] Perform cluster analysis on user access cycle data and user access trend data to generate user access pattern data;

[0064] Collect historical access data of user access pattern data to obtain user historical access pattern data; use user historical access pattern data to mine association rules on user access pattern data to generate user access behavior data.

[0065] The present invention uses a time series pattern recognition technology to identify the user's access time series pattern by targeting the e-commerce transaction data flow diagram. This includes the periodicity, trend and other characteristics of user access. According to the identified time series pattern, user access time series data is generated. These data include the user's access cycle data (such as weekly and monthly access frequency) and the user's access trend data (such as the growth or decline trend of the number of visits). Cluster analysis is performed on the generated user access cycle data and user access trend data, and users are divided into different groups or categories to generate user access pattern data. This helps to identify the access behavior characteristics and patterns of different groups. The user's historical access data is collected, including past access behaviors and transaction records, specifically the user's browsing records, purchase records, etc. The user's historical access pattern data is used to perform association rule mining on the user access pattern data. By analyzing the user's historical behavior, the association rules between different access patterns are found, such as the probability of browsing related products after purchasing a certain commodity. According to the results of association rule mining, user access behavior data is generated. These data reflect the access behavior associations and patterns between users, which helps to understand the user's behavioral habits and preferences.

[0066] In an embodiment of the present invention, by using a time series analysis method (such as ARIMA, Exponential Smoothing, etc.) or a pattern recognition algorithm (such as dynamic time warping, time series clustering, etc.), the e-commerce transaction data flow graph is subjected to time series pattern recognition. This can identify the periodicity and trend of user access. The identified periodicity and trend features are extracted to generate user access period data and user access trend data. Cluster analysis is performed on the user access period data and user access trend data. Commonly used methods include K-means clustering, hierarchical clustering, etc. According to the clustering results, users are divided into different groups or categories to generate user access pattern data. The user's historical access data is collected from the log or database of the e-commerce system, including browsing records, purchase records, search records, etc. Ensure that the collected historical access data is representative and covers the access behaviors of different types of users and different time periods. Utilize the collected user historical access pattern data and use association rule mining algorithms (such as Apriori algorithm, FP-Growth algorithm, etc.) to discover association rules between user access patterns. By mining the correlation between users' historical access data, user access behavior data is generated, including users' typical behavior sequences, purchase behavior patterns, etc.

[0067] Preferably, step S24 includes the following steps:

[0068] Step S241: performing user access identification on the user access pattern data and the user access behavior data to generate user access identification data, wherein the user access identification data includes user access role data and user access permission data; dividing user role groups according to the user access role data and the user access permission data to generate user role groups;

[0069] Step S242: classifying resource contents according to the e-commerce transaction data flow diagram to generate e-commerce transaction resource content classification data; mapping role access rights to the e-commerce transaction resource content classification data using user role groups to generate a role access rights mapping table;

[0070] Step S243: Refine the user access control policy based on the role permission mapping table to generate a first access control policy and a second access control policy; integrate the first access control policy and the second access control policy to generate a multi-level access control policy for e-commerce transactions.

[0071] The present invention identifies the user's access role and authority based on the user's access mode and behavior data for subsequent authority management and control. By analyzing the e-commerce transaction data flow diagram, the resource content is classified to better understand the resources in the system and their associations. The resource content classification data is mapped using user role groups to determine the access rights of each role to different resources, providing a basis for subsequent access control strategies. Based on the role authority mapping table, the user's access control strategy is refined, including the first and second layer access control strategies, to ensure accurate control of access to different roles. The first and second layer access control strategies are integrated into a multi-level access control strategy to comprehensively consider the security requirements of the system and the user's access experience.

[0072] As an example of the present invention, refer to Figure 3 As shown, in this example, step S24 includes:

[0073] Step S241: performing user access identification on the user access pattern data and the user access behavior data to generate user access identification data, wherein the user access identification data includes user access role data and user access permission data; dividing user role groups according to the user access role data and the user access permission data to generate user role groups;

[0074] In the embodiment of the present invention, the user's access behavior is identified and analyzed by using the user access pattern data and the user access behavior data. The user's access role and access rights are identified based on the user's access behavior. The identified user access role data and user access rights data are sorted and summarized to form user access identification data. User role groups are divided based on the user access role data and user access rights data. Users with similar access characteristics and permission requirements are divided into the same role group. The differences and relationships between role groups are determined to facilitate the subsequent design and implementation of access control policies.

[0075] Step S242: classifying resource contents according to the e-commerce transaction data flow diagram to generate e-commerce transaction resource content classification data; mapping role access rights to the e-commerce transaction resource content classification data using user role groups to generate a role access rights mapping table;

[0076] In an embodiment of the present invention, the resource content involved in the transaction is classified by using an e-commerce transaction data flow diagram. According to the nature, function, type and other characteristics of the resources, the transaction resources are divided into different categories or classifications, and the resource content is classified using a machine learning algorithm, text mining technology or domain knowledge. The classified resource content is organized into e-commerce transaction resource content classification data. The data should include information such as the name, type, description, and access rights of the resource content for subsequent permission mapping and access control. The e-commerce transaction resource content classification data is mapped to role access rights using user role group information. Determine the access rights and operation rights of each role group to different types of resource content, such as viewing, modifying, deleting, etc. Establish a permission mapping relationship between the user role group and the resource content classification to form a role permission mapping table.

[0077] Step S243: Refine the user access control policy based on the role permission mapping table to generate a first access control policy and a second access control policy; integrate the first access control policy and the second access control policy to generate a multi-level access control policy for e-commerce transactions.

[0078] In the embodiment of the present invention, a specific access control policy is formulated for each role group according to the information in the role permission mapping table. The first access control policy involves access control based on identity authentication, such as user login authentication. The second access control policy involves access control based on role, that is, limiting the user's access to resources according to the permissions of the role group to which the user belongs. The first access control policy specifically includes measures such as identity authentication, session management, and access tokens to ensure the identity and legitimacy of the user. For example, when a user accesses an e-commerce platform, the user name and password need to be verified to ensure that only legitimate users can access the system. The second access control policy formulates detailed access control rules according to the role group to which the user belongs and the permission requirements of the resources. According to the role permission mapping table, each role group is assigned corresponding resource access rights, including operations such as reading, modifying, and deleting. The first access control policy and the second access control policy are integrated to form a multi-level access control policy. The first-level policy is responsible for authenticating the user and managing the access session to ensure the security and stability of the system. The second-level policy is responsible for limiting the user's access to resources according to the role group to which the user belongs and the resource permission requirements to ensure the data security and privacy protection of the system.

[0079] Preferably, the step of refining the user access control policy based on the role permission mapping table includes:

[0080] Obtain user information data and system resource data based on the role permission mapping table; define user roles based on the user information data and generate user role definition data, wherein the user role definition data includes administrators, ordinary users and data analysts;

[0081] Divide the system resource data into permission sets to generate a system resource permission set; use the system resource permission set to assign role permissions to the user role definition data to generate role permission assignment data; perform role-based access control on the user information data through the role permission assignment data to generate a first access control policy;

[0082] Classifying the system resource data by resource content to generate system resource content classification data, wherein the system resource content classification includes resource attribute classification and resource sensitivity classification;

[0083] Perform user feature analysis on the user information data to generate user feature data; perform content-based access control based on the system resource content classification data and the user feature data to generate a second access control policy.

[0084] The present invention can clearly define the scope of authority of each user and reduce confusion and misunderstanding by dividing users into roles such as administrators, ordinary users and data analysts. After dividing the system resource data into permission sets, it is easier to manage and allocate permissions to ensure that each user can only access the resources they need, thereby improving security and management efficiency. By applying the role permission allocation data to the user information data, a first access control policy, that is, a role-based access control policy, can be established, which helps to ensure the security and compliance of the system. After the system resources are content-classified, access to different types of resources can be more finely controlled, which improves the system's level of protection for sensitive data and also enhances the management and monitoring capabilities of resources. By establishing a second access control policy based on user feature data and resource content classification data, more personalized and refined access control can be achieved to ensure that users can only access resources that match their roles and features, thereby improving the flexibility and security of the system.

[0085] In the embodiment of the present invention, user information data and system resource data are obtained from the system, including user identity information, role information and various resources existing in the system. According to the role permission mapping table, the user information data is analyzed and processed to define the user's role. Common roles include administrators, ordinary users, data analysts, etc. The system resource data is divided into permission sets, and the permission requirements of different resources are classified and summarized to form a system resource permission set. The system resource permission set is used to assign corresponding access rights to each user role definition data. According to the role permission mapping table, the access rights and operation rights of each role group to the system resources are determined. According to the role permission allocation data, the user information data is subjected to role-based access control. The resource scope and permission restrictions that each role can access are determined, and a first access control policy is generated. The system resource data is subjected to resource content classification, and different resources are classified and divided according to their attributes. This includes resource attribute classification and resource sensitivity classification. User information data is subjected to user feature analysis to understand the user's behavior pattern, preference and characteristics. User feature data is generated to provide a basis for subsequent content-based access control. Combining the system resource content classification data and user feature data, a content-based access control policy is formulated. According to resource attributes and user characteristics, the user's access rights to resources are finely controlled to generate a second access control policy.

[0086] Preferably, step S3 comprises the following steps:

[0087] Step S31: Based on the multi-level access control strategy of e-commerce transactions, access flow monitoring is performed on the e-commerce transaction data flow graph to obtain hierarchical access flow monitoring data;

[0088] Step S32: divide the hierarchical access traffic monitoring data into data sets to generate a model training set and a model test set; use the random forest algorithm to train the model training set to generate a hierarchical access traffic training model; optimize and iterate the hierarchical access traffic training model through the model test set to generate a hierarchical access traffic prediction model;

[0089] Step S33: importing the hierarchical access traffic monitoring data into the hierarchical access traffic prediction model to perform traffic prediction and generate hierarchical access traffic prediction data;

[0090] Step S34: Analyze the access period of the hierarchical access traffic prediction data to generate normal traffic access period and abnormal traffic access period; respond to the abnormal traffic access period with abnormal traffic, thereby generating abnormal traffic warning data.

[0091] The present invention can monitor the e-commerce transaction data flow graph to understand the system access situation in real time, including which level of data is accessed, access frequency and other information. The monitoring data is trained and optimized by machine learning techniques such as random forest algorithm to generate a hierarchical access traffic prediction model to predict future access traffic. The monitoring data is imported into the trained prediction model to perform traffic prediction, and the predicted data is analyzed for access time periods to determine the normal traffic access time period and the abnormal traffic access time period, so as to better manage system resources. For the abnormal traffic access time period, the system can implement corresponding abnormal traffic response measures, such as increasing server capacity, blocking abnormal access sources, etc., to ensure the stability and security of the system. By generating abnormal traffic warning data, the system can promptly discover potential security risks and problems, take preventive measures, and avoid adverse consequences such as data leakage and service interruption.

[0092] In the embodiment of the present invention, the e-commerce transaction data flow graph is monitored according to the multi-level access control strategy of e-commerce transactions to identify abnormal access behavior. Specifically, the monitoring is implemented by using network traffic monitoring tools, log analysis systems and other technologies. The hierarchical access traffic monitoring data is divided into a model training set and a model test set, usually divided in chronological order to ensure the generalization ability of the model on future traffic data. The model training set is trained using a random forest algorithm to generate a hierarchical access traffic training model. The trained model is tested using a model test set to evaluate the performance of the model, and the model parameters are tuned and optimized according to the test results. Specifically, cross-validation and other technologies are used to evaluate the model performance and select the best model. The hierarchical access traffic monitoring data is imported into the optimized hierarchical access traffic prediction model for traffic prediction to generate hierarchical access traffic prediction data. Specifically, time series prediction models, machine learning algorithms and other technologies are used for traffic prediction. The hierarchical access traffic prediction data is analyzed for access period to identify normal traffic access period and abnormal traffic access period. For abnormal traffic access period, abnormal traffic response measures are implemented, such as increasing security verification, restricting access rights, etc., to generate abnormal traffic warning data.

[0093] Preferably, step S34 includes the following steps:

[0094] Step S341: Calculate the traffic difference of the hierarchical access traffic monitoring data and the hierarchical access traffic prediction data at the same timestamp to obtain the actual access traffic difference;

[0095] Step S342: Compare the actual access traffic difference with the preset traffic difference threshold. When the actual access traffic difference is less than or equal to the preset traffic difference threshold, mark the actual access traffic difference with a corresponding time period to generate a normal traffic access period.

[0096] Step S343: when the actual access traffic difference is greater than the preset traffic difference threshold, the actual access traffic difference is marked with a corresponding time period to generate an abnormal traffic access period;

[0097] Step S344: Perform abnormal time domain analysis on the abnormal traffic access period to generate abnormal traffic time domain data; perform alarm response processing based on the abnormal traffic time domain data to generate abnormal traffic warning data.

[0098] By calculating the difference between actual access traffic and predicted traffic, the present invention can understand the deviation between actual conditions and expectations, thereby better evaluating the operating status of the system. According to the preset traffic difference threshold, the actual access traffic difference is classified and marked to generate normal traffic access time periods and abnormal traffic access time periods, so that the system can automatically identify potential abnormal situations. Time domain analysis of abnormal traffic access time periods can provide an in-depth understanding of the time patterns and regularities of abnormal occurrences, which helps to further identify the causes and characteristics of abnormal traffic. Based on the abnormal traffic time domain data for alarm response processing, the system can promptly issue an alarm to notify relevant personnel or system administrators and take corresponding countermeasures to reduce the impact of abnormal traffic on system security and performance.

[0099] In an embodiment of the present invention, by comparing the hierarchical access traffic monitoring data and the hierarchical access traffic prediction data at the same timestamp, the difference between the actual access traffic and the predicted traffic is calculated, and the actual access traffic difference is calculated specifically by simple mathematical operations, such as subtraction operations. The actual access traffic difference is compared with the preset traffic difference threshold. When the actual access traffic difference is less than or equal to the preset traffic difference threshold, the time period is marked as a normal traffic access period. When the actual access traffic difference is greater than the preset traffic difference threshold, the time period is marked as an abnormal traffic access period. An abnormal time domain analysis is performed on the abnormal traffic access period, including the source, type, duration, etc. of the abnormal traffic. Based on the abnormal traffic time domain data, an alarm response process is performed, and a variety of specific methods are adopted, such as sending an alarm notification, restricting access rights, recording abnormal logs, etc., to generate abnormal traffic warning data.

[0100] Preferably, step S4 comprises the following steps:

[0101] Step S41: elastically distribute and store the e-commerce transaction encrypted data based on the normal traffic access period to generate e-commerce transaction storage data;

[0102] Step S42: backing up the e-commerce transaction storage data according to a preset time interval to generate e-commerce transaction backup data;

[0103] Step S43: using the e-commerce transaction backup data to perform backup coverage detection on the e-commerce transaction storage data, and generating electronic transaction backup coverage detection data;

[0104] Step S44: E-commerce transaction security assessment is performed through abnormal traffic warning data and electronic transaction backup coverage detection data to perform e-commerce transaction data security supervision operations.

[0105] The present invention improves the security and reliability of data by storing encrypted e-commerce transaction data in an elastic distributed storage system based on normal traffic access periods, while also enhancing the scalability and flexibility of the system. E-commerce transaction data is backed up according to preset time intervals to ensure the persistence and recoverability of data to cope with unexpected situations and catastrophic events and ensure the stable operation of the system. The storage data is overwritten using the e-commerce transaction backup data to ensure the integrity and accuracy of the backup data, thereby improving the credibility and effectiveness of the data backup. Combined with abnormal traffic warning data and backup overwrite detection data, the security of e-commerce transaction data is evaluated, potential security risks and problems are discovered in a timely manner, and reference basis and decision-making support are provided for the security management of the system. By performing security assessments, the system can identify and resolve security risks in a timely manner, strengthen the supervision and control of e-commerce transaction data, protect the privacy and security of user data, and enhance user trust and satisfaction.

[0106] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0107] Step S41: elastically distribute and store the e-commerce transaction encrypted data based on the normal traffic access period to generate e-commerce transaction storage data;

[0108] In an embodiment of the present invention, a resilient distributed storage system is designed to store e-commerce transaction data. Consider using existing distributed storage technologies, such as Hadoop HDFS, Apache Cassandra, Amazon S3, etc., or develop a distributed storage system yourself. Partition the e-commerce transaction data according to normal traffic access periods so that the data can be distributed and stored on different storage nodes. Partition the data specifically according to factors such as time and geographic location. Deploy multiple storage nodes in the distributed storage system to ensure that the system has high availability and fault tolerance. The number and size of nodes can be adjusted according to the amount of data and performance requirements. Store the e-commerce transaction data on each storage node, and ensure the persistence and reliability of the data.

[0109] Step S42: backing up the e-commerce transaction storage data according to a preset time interval to generate e-commerce transaction backup data;

[0110] In an embodiment of the present invention, a backup strategy is designed to determine the backup time interval, the backup data range and the backup target location. Consider factors such as backup frequency, backup integrity and recoverability. Select a suitable data backup tool or system to perform the backup operation. Commonly used backup tools include rsync, Bacula, Veeam Backup, etc., and the backup service of a cloud service provider can also be used. Configure a scheduled backup task to automatically trigger the backup operation according to a preset time interval. Specifically, use the scheduled task tool (such as cron) of the operating system or the scheduled backup function provided by the backup software to achieve this. Divide the backup range of the e-commerce transaction storage data according to the preset time interval. Specifically, backup is performed according to time periods such as days, weeks, and months, and the backup cycle can also be customized according to business needs. Store the backup data in a safe and reliable location, specifically local storage, network storage, or cloud storage. Ensure the integrity and accessibility of the backup data so that data can be restored when needed.

[0111] Step S43: using the e-commerce transaction backup data to perform backup coverage detection on the e-commerce transaction storage data, and generating electronic transaction backup coverage detection data;

[0112] In an embodiment of the present invention, a data comparison algorithm is designed to compare the difference between the backup data and the original storage data. Common comparison algorithms include content-based comparison, hash algorithm (such as MD5, SHA-256) comparison, etc. Configure the backup overwrite detection task and perform the backup overwrite detection operation regularly. Determine the time interval and frequency of the detection, as well as the data range of the detection. Compare the e-commerce transaction backup data with the original storage data to detect the difference between the backup data and the original data. The comparison can be performed file by file or data block by data block to ensure the integrity and consistency of the backup data. Based on the comparison results, generate backup overwrite detection data and record the coverage of the backup data. Including records of the backup data completely covering the original data, partially covering, not covering, etc.

[0113] Step S44: E-commerce transaction security assessment is performed through abnormal traffic warning data and electronic transaction backup coverage detection data to perform e-commerce transaction data security supervision operations.

[0114] In an embodiment of the present invention, the abnormal traffic warning data and the electronic transaction backup coverage detection data generated in step S43 are integrated together for comprehensive analysis and evaluation. Security assessment indicators and evaluation models are designed to evaluate the security of e-commerce transaction data. The security assessment indicators specifically include indicators of data integrity, availability, confidentiality, etc., and the evaluation model specifically adopts quantitative analysis or qualitative evaluation methods. The integrated data is analyzed and evaluated to identify potential security risks and problems. The abnormal situation is analyzed according to the abnormal traffic warning data to determine the existing security threats and attack behaviors; the integrity and recoverability of the backup data are evaluated according to the backup coverage detection data. Based on the data analysis and evaluation results, a security assessment report is generated, which describes the evaluation process, the security issues found, and the recommended solutions in detail. The report specifically includes a description of the abnormal situation, risk analysis, security recommendations, etc. According to the recommendations in the evaluation report, corresponding risk management and control measures are formulated to reduce security risks and improve data security, including measures such as strengthening access control, improving backup strategies, and strengthening security monitoring.

[0115] In this specification, a transaction data security supervision system for e-commerce is provided, which is used to execute the above-mentioned transaction data security supervision method for e-commerce. The transaction data security supervision system for e-commerce includes:

[0116] The transaction complexity calculation module is used to obtain the original e-commerce transaction data; perform data segment complexity calculation on the original e-commerce transaction data to generate the complexity of the e-commerce transaction data segment; perform complexity integration on the complexity of the e-commerce transaction data segment to generate the e-commerce transaction encryption entropy index; perform hash conversion on the e-commerce transaction encryption entropy index to generate the e-commerce transaction encryption entropy hash data;

[0117] The multi-layer access control module is used to perform data security encryption on standard e-commerce transaction data according to the e-commerce transaction encryption entropy hash data to generate e-commerce transaction encrypted data; perform data transmission flow analysis on the e-commerce transaction encrypted data to generate an e-commerce transaction data flow graph; construct a multi-layer access control strategy according to the e-commerce transaction data flow graph to generate a multi-layer access control strategy for e-commerce transactions;

[0118] The traffic monitoring module is used to monitor the access traffic of the e-commerce transaction data traffic graph based on the multi-level access control strategy of the e-commerce transaction, and obtain the hierarchical access traffic monitoring data; perform traffic prediction on the hierarchical access traffic monitoring data to generate hierarchical access traffic prediction data; perform access period analysis on the hierarchical access traffic prediction data to generate normal traffic access period and abnormal traffic access period; perform abnormal traffic response on the abnormal traffic access period, thereby generating abnormal traffic warning data;

[0119] The transaction security assessment module is used to perform elastic distributed storage of e-commerce transaction encrypted data based on normal traffic access periods to generate e-commerce transaction storage data; perform backup coverage detection on e-commerce transaction storage data according to preset time intervals to generate electronic transaction backup coverage detection data; perform e-commerce transaction security assessment through abnormal traffic warning data and electronic transaction backup coverage detection data to perform e-commerce transaction data security supervision operations.

[0120] The beneficial effect of the present invention is that by performing complexity calculation, encryption and multi-level access control strategy construction on e-commerce transaction data, the security of data is effectively improved, and the transaction data is protected from unauthorized access or tampering. Through traffic monitoring, prediction and abnormal traffic response, abnormal traffic behavior can be discovered in time, and corresponding measures can be taken to ensure the normal operation and security of e-commerce transaction data. Through elastic distributed storage of backup data and regular backup coverage detection, the integrity and recoverability of transaction data are guaranteed, ensuring that data can be restored in time when it is lost or damaged. Security assessment and supervision are performed through abnormal traffic warning data and backup coverage detection data, the security status of transaction data can be fully understood, security issues can be discovered and resolved in time, and the level of data security management can be improved. Therefore, the present invention improves the security of e-commerce transaction supervision by utilizing data complexity calculation, multi-level access control, and abnormal traffic monitoring.

[0121] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0122] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A transaction data security supervision method for e-commerce, characterized in that: The following steps are involved: Step S1: obtaining original e-commerce transaction data; performing data segment complexity calculation on the original e-commerce transaction data to generate e-commerce transaction data segment complexity; performing complexity integration on the e-commerce transaction data segment complexity to generate an e-commerce transaction encryption entropy index; performing hash conversion on the e-commerce transaction encryption entropy index to generate e-commerce transaction encryption entropy hash data; Step S2: Perform data security encryption on standard e-commerce transaction data according to the e-commerce transaction encryption entropy hash data to generate e-commerce transaction encrypted data; perform data transmission flow analysis on the e-commerce transaction encrypted data to generate an e-commerce transaction data flow graph; construct a multi-level access control strategy according to the e-commerce transaction data flow graph to generate an e-commerce transaction multi-level access control strategy; Step S2 includes the following steps: Step S21: performing data security encryption on standard e-commerce transaction data according to the e-commerce transaction encryption entropy hash data to generate e-commerce transaction encrypted data; Step S22: Based on the IFP technology, the transaction data flow is monitored on the e-commerce transaction encrypted data to obtain the e-commerce transaction data flow; the data transmission flow is analyzed on the e-commerce transaction data flow to generate an e-commerce transaction data flow graph; Step S23: performing user access pattern recognition on the e-commerce transaction data flow graph to generate user access pattern data; performing user access behavior analysis on the user access pattern data to generate user access behavior data; Step S23 includes the following steps: Perform time series pattern recognition on the e-commerce transaction data flow graph to generate user access time series data, where the user access time series data includes user access cycle data and user access trend data; Perform cluster analysis on user access cycle data and user access trend data to generate user access pattern data; Collect historical access data of user access pattern data to obtain historical access pattern data of users; use historical access pattern data of users to mine association rules on user access pattern data to generate user access behavior data; Step S24: construct a multi-level access control strategy based on the user access pattern data and the user access behavior data to generate a multi-level access control strategy for e-commerce transactions; Step S24 includes the following steps: Step S241: performing user access identification on the user access pattern data and the user access behavior data to generate user access identification data, wherein the user access identification data includes user access role data and user access permission data; dividing user role groups according to the user access role data and the user access permission data to generate user role groups; Step S242: classifying resource contents according to the e-commerce transaction data flow diagram to generate e-commerce transaction resource content classification data; mapping role access rights to the e-commerce transaction resource content classification data using user role groups to generate a role access rights mapping table; Step S243: Refine the user access control policy based on the role permission mapping table to generate a first access control policy and a second access control policy; integrate the first access control policy and the second access control policy to generate a multi-level access control policy for e-commerce transactions; Step S3: Based on the multi-level access control strategy of e-commerce transactions, access traffic is monitored on the e-commerce transaction data flow graph to obtain hierarchical access traffic monitoring data; traffic is predicted on the hierarchical access traffic monitoring data to generate hierarchical access traffic prediction data; access period analysis is performed on the hierarchical access traffic prediction data to generate normal traffic access period and abnormal traffic access period; abnormal traffic response is performed on the abnormal traffic access period to generate abnormal traffic warning data; Step S4: elastically distribute and store the encrypted data of e-commerce transactions based on normal traffic access periods to generate e-commerce transaction storage data; perform backup coverage detection on the e-commerce transaction storage data at preset time intervals to generate electronic transaction backup coverage detection data; conduct e-commerce transaction security assessments based on abnormal traffic warning data and electronic transaction backup coverage detection data to perform e-commerce transaction data security supervision operations.

2. The transaction data security supervision method for e-commerce according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtaining original e-commerce transaction data; Step S12: preprocessing the original e-commerce transaction data to generate standard e-commerce transaction data, wherein the data preprocessing includes data denoising, data missing value filling, data normalization and data standardization; Step S13: segmenting the standard e-commerce transaction data into data segments to obtain e-commerce transaction data segments; transmitting the e-commerce transaction data segments to the distributed server nodes to perform data complexity calculation to generate the complexity of the e-commerce transaction data segments; Step S14: performing complexity integration on the complexity of the e-commerce transaction data fragments to generate an e-commerce transaction encryption entropy index; performing hash conversion on the e-commerce transaction encryption entropy index to generate e-commerce transaction encryption entropy hash data.

3. The transaction data security supervision method for e-commerce according to claim 1 is characterized in that: The steps for refining the user access control policy based on the role permission mapping table include: Obtain user information data and system resource data based on the role permission mapping table; define user roles based on the user information data and generate user role definition data, wherein the user role definition data includes administrators, ordinary users and data analysts; Divide the system resource data into permission sets to generate a system resource permission set; use the system resource permission set to assign role permissions to the user role definition data to generate role permission assignment data; perform role-based access control on the user information data through the role permission assignment data to generate a first access control policy; Classifying the system resource data by resource content to generate system resource content classification data, wherein the system resource content classification includes resource attribute classification and resource sensitivity classification; Perform user feature analysis on the user information data to generate user feature data; perform content-based access control based on the system resource content classification data and the user feature data to generate a second access control policy.

4. The transaction data security supervision method for e-commerce according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Based on the multi-level access control strategy of e-commerce transactions, access flow monitoring is performed on the e-commerce transaction data flow graph to obtain hierarchical access flow monitoring data; Step S32: divide the hierarchical access traffic monitoring data into data sets to generate a model training set and a model test set; use the random forest algorithm to train the model training set to generate a hierarchical access traffic training model; optimize and iterate the hierarchical access traffic training model through the model test set to generate a hierarchical access traffic prediction model; Step S33: importing the hierarchical access traffic monitoring data into the hierarchical access traffic prediction model to perform traffic prediction and generate hierarchical access traffic prediction data; Step S34: Analyze the access period of the hierarchical access traffic prediction data to generate normal traffic access period and abnormal traffic access period; respond to the abnormal traffic access period with abnormal traffic, thereby generating abnormal traffic warning data.

5. The transaction data security supervision method for e-commerce according to claim 4 is characterized in that: Step S34 includes the following steps: Step S341: Calculate the traffic difference of the hierarchical access traffic monitoring data and the hierarchical access traffic prediction data at the same timestamp to obtain the actual access traffic difference; Step S342: Compare the actual access traffic difference with the preset traffic difference threshold. When the actual access traffic difference is less than or equal to the preset traffic difference threshold, mark the actual access traffic difference with a corresponding time period to generate a normal traffic access period. Step S343: when the actual access traffic difference is greater than the preset traffic difference threshold, the actual access traffic difference is marked with a corresponding time period to generate an abnormal traffic access period; Step S344: Perform abnormal time domain analysis on the abnormal traffic access period to generate abnormal traffic time domain data; perform alarm response processing based on the abnormal traffic time domain data to generate abnormal traffic warning data.

6. The transaction data security supervision method for e-commerce according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: elastically distribute and store the e-commerce transaction encrypted data based on the normal traffic access period to generate e-commerce transaction storage data; Step S42: backing up the e-commerce transaction storage data according to a preset time interval to generate e-commerce transaction backup data; Step S43: using the e-commerce transaction backup data to perform backup coverage detection on the e-commerce transaction storage data, and generating electronic transaction backup coverage detection data; Step S44: E-commerce transaction security assessment is performed through abnormal traffic warning data and electronic transaction backup coverage detection data to perform e-commerce transaction data security supervision operations.

7. A transaction data security supervision system for e-commerce, characterized in that: The method for securely supervising transaction data for e-commerce according to claim 1 comprises: The transaction complexity calculation module is used to obtain the original e-commerce transaction data; perform data segment complexity calculation on the original e-commerce transaction data to generate the complexity of the e-commerce transaction data segment; perform complexity integration on the complexity of the e-commerce transaction data segment to generate the e-commerce transaction encryption entropy index; perform hash conversion on the e-commerce transaction encryption entropy index to generate the e-commerce transaction encryption entropy hash data; The multi-layer access control module is used to perform data security encryption on standard e-commerce transaction data according to the e-commerce transaction encryption entropy hash data to generate e-commerce transaction encrypted data; perform data transmission flow analysis on the e-commerce transaction encrypted data to generate an e-commerce transaction data flow graph; construct a multi-layer access control strategy according to the e-commerce transaction data flow graph to generate a multi-layer access control strategy for e-commerce transactions; The traffic monitoring module is used to monitor the access traffic of the e-commerce transaction data traffic graph based on the multi-level access control strategy of the e-commerce transaction, and obtain the hierarchical access traffic monitoring data; perform traffic prediction on the hierarchical access traffic monitoring data to generate hierarchical access traffic prediction data; perform access period analysis on the hierarchical access traffic prediction data to generate normal traffic access period and abnormal traffic access period; perform abnormal traffic response on the abnormal traffic access period, thereby generating abnormal traffic warning data; The transaction security assessment module is used to perform elastic distributed storage of e-commerce transaction encrypted data based on normal traffic access periods to generate e-commerce transaction storage data; perform backup coverage detection on e-commerce transaction storage data according to preset time intervals to generate electronic transaction backup coverage detection data; perform e-commerce transaction security assessment through abnormal traffic warning data and electronic transaction backup coverage detection data to perform e-commerce transaction data security supervision operations.

Images