Network security analysis system based on big data

Abstract

The invention discloses a network security analysis system based on big data. The network security analysis system comprises an identity verification module, a data acquisition module, a data extraction module, an analysis processing module, an evaluation module and a big data storage module. The identity verification module is used for accurately verifying the identity of the user so as to effectively prevent unauthorized access and data leakage; the data acquisition module is used for collecting various data in a network and providing a basis for subsequent security analysis; the data extraction module is used for efficiently extracting and integrating data from various data sources for subsequent analysis; the analysis processing module is used for deeply analyzing and processing data extracted and transmitted from each data source; the system pays attention to cooperative work among the modules in design, an efficient and stable whole is formed, and due to the collaboration, the overall performance of the system is improved, and the flexibility and expandability of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of network security analysis systems, and in particular to a network security analysis system based on big data. Background Art

[0002] With the innovative development and popular application of the Internet, the importance of maintaining and protecting network security has become increasingly prominent. At this stage, the amount of network information data is growing and the speed is accelerating, which puts higher requirements on the current network security analysis work. As a new technology, big data technology has been widely used in network security analysis work. The widespread application of big data technology has provided new impetus for network security analysis and defense, and has become an indispensable existence in the construction of network security analysis system.

[0003] The network security analysis system is an efficient and intelligent security solution that uses big data processing technology, machine learning algorithms and real-time data analysis capabilities to conduct in-depth mining and analysis of massive network data to identify potential security threats, abnormal behaviors and network attacks. This system is crucial to protecting corporate assets, user privacy and data security.

[0004] However, the existing network security analysis system based on big data still has some shortcomings, such as the inability to conduct real-time security monitoring of network data transmission in a big data environment, which may lead to data theft or tampering during transmission. At the same time, it is impossible to conduct a comprehensive and in-depth assessment and risk prediction of network security. Therefore, we propose a network security analysis system based on big data. Summary of the invention

[0005] The purpose of the present invention is to provide a network security analysis system based on big data to overcome the technical problems existing in the prior art.

[0006] In order to achieve the above technical objectives and the above technical effects, the present invention provides the following technical solutions:

[0007] A network security analysis system based on big data, the network security analysis system includes an identity authentication module, a data acquisition module, a data extraction module, an analysis and processing module, an evaluation module and a big data storage module;

[0008] The identity verification module is used to accurately verify the user's identity, thereby effectively preventing unauthorized access and data leakage;

[0009] The data acquisition module is used to collect various data in the network to provide a basis for subsequent security analysis;

[0010] The data extraction module is used to efficiently extract and integrate data from various data sources for subsequent analysis;

[0011] The analysis and processing module is used to perform in-depth analysis and processing on the data extracted and transmitted from various data sources;

[0012] The evaluation module is used to evaluate the security situation and predict the risks of the processed data;

[0013] The big data storage module is responsible for efficiently storing and managing a large amount of network security data.

[0014] Preferably, in a network security analysis system based on big data, the identity authentication module includes a face recognition unit and an account and password login unit, the face recognition unit is used to capture the user's facial image and verify the user's identity after comparison, and the account and password login unit is used to enter the correct account number and password to verify the identity.

[0015] Preferably, in a network security analysis system based on big data, the data acquisition module includes a text acquisition unit and an image acquisition unit, the text acquisition unit is used to acquire data from a file system, and the image acquisition unit is used to capture image data in a network environment.

[0016] Preferably, in a network security analysis system based on big data, the data extraction module includes a desensitizing unit and a screening unit, the desensitizing unit is used to desensitize sensitive information during the data extraction process, and the screening unit is used to identify and screen the data to be extracted according to preset rules and conditions.

[0017] Preferably, in a network security analysis system based on big data, the analysis and processing module includes a preprocessing unit and an online processing unit, the preprocessing unit is used to remove noise and redundant information in the data, and the online processing unit is used to perform online processing and analysis on the extracted data to identify potential threats and abnormal behaviors in the network.

[0018] Preferably, in a network security analysis system based on big data, the evaluation module includes an early warning unit and a feedback unit, the early warning unit is used to issue early warning information to notify relevant personnel to take countermeasures, and the feedback unit is used to classify, filter and prioritize the early warning information.

[0019] The beneficial effects of the present invention are:

[0020] 1. The system is reasonably designed. Through the design of sophisticated identity authentication modules, the system can ensure that only legitimate users can access system resources, which greatly improves the security of the system, effectively improves the accuracy and security of identity authentication, provides strong security protection for the system, and effectively prevents unauthorized access and potential security threats;

[0021] 2. This system collects all kinds of data in the network comprehensively and in real time through the data acquisition module, ensuring that the system can capture the latest dynamics and potential threats in the network. At the same time, the efficient operation of the data extraction module enables the system to quickly extract valuable information from various complex data sources, protect the security of sensitive data through desensitization processing, and reduce the risk of data leakage. The preset rules and conditions help the system to intelligently screen data, avoid unnecessary data processing burdens, and improve overall analysis efficiency;

[0022] 3. This system uses efficient data processing technology and algorithms, which can quickly process massive amounts of data and meet the high real-time requirements of network security analysis. This real-time performance is essential for timely detection and response to network attacks. In addition, the introduction of the evaluation module enables the system to conduct a comprehensive and in-depth evaluation and risk prediction of network security status based on data analysis results, providing a scientific basis and strong support for the formulation and adjustment of security strategies;

[0023] To sum up, the design of this system focuses on the collaborative work between modules to form an efficient and stable whole. The identity authentication module, data acquisition module, data extraction module, analysis and processing module, as well as the evaluation module are closely linked to form a complete network security analysis system. This synergy not only improves the overall performance of the system, but also enhances the flexibility and scalability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the description of the specific implementation methods will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 This is a schematic diagram of the structure of the network security analysis system based on big data. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] See also Figure 1As shown, this embodiment is a network security analysis system based on big data, which includes an identity authentication module, a data acquisition module, a data extraction module, an analysis and processing module, an evaluation module and a big data storage module; the identity authentication module is used to accurately verify the user identity, thereby effectively preventing unauthorized access and data leakage; the data acquisition module is used to collect various data in the network to provide a basis for subsequent security analysis; the data extraction module is used to efficiently extract and integrate data from various data sources for subsequent analysis; the analysis and processing module is used to perform in-depth analysis and processing on the data extracted and transmitted from various data sources; the evaluation module is used to evaluate the security situation and predict the risks of the processed data; the big data storage module is responsible for efficiently storing and managing a large amount of network security data.

[0028] The main function of the identity authentication module is to judge and process the legitimacy of the user, ensuring that only authorized users can access system resources. This module plays a vital role in the network security analysis system because it is the first line of defense for network security and can prevent a large number of potential security threats. In the network security analysis system based on big data, the identity authentication module can adopt a variety of advanced identity authentication technologies to improve the accuracy and security of verification. The identity authentication module is a key component to ensure system security. By adopting a strategy that combines a variety of advanced identity authentication technologies with big data analysis technologies, the accuracy and security of identity authentication can be effectively improved, providing a strong security guarantee for the system.

[0029] The data collection module is used to collect various data in the network and provide a basis for subsequent security analysis. The data collection module is a vital component of the network security analysis system. It provides a solid foundation for subsequent security analysis by collecting various data in the network in a comprehensive and real-time manner. At the same time, attention should be paid to ensuring the security and compliance of the data during the data collection process. During the data collection process, special attention should be paid to encrypting the transmission and storage of sensitive data to prevent data leakage, setting strict access control policies, limiting access rights to the collected data, and desensitizing sensitive information, such as hiding user identities, blurring key data, etc., to ensure that data collection activities comply with relevant laws, regulations and industry standards.

[0030] The data extraction module is used to efficiently extract and integrate data from various data sources for subsequent analysis. In the process of transmitting data from the data source to the data extraction module, encryption technology (such as SSL / TLS protocol) is used to ensure the security of data transmission and prevent data from being stolen or tampered with during transmission. In the data extraction process, sensitive information is desensitized, such as hiding, replacing or deleting key data fields to reduce the risk of privacy leakage. The data to be extracted is identified and screened according to preset rules and conditions, which helps to reduce unnecessary data processing burden and improve analysis efficiency. Data is extracted from the selected data source and necessary preprocessing such as format conversion, deduplication, and cleaning is performed. Then, the extracted data is integrated into a unified data format for subsequent analysis and processing. While ensuring data privacy and security, the data extraction module also needs to have efficient and flexible data processing capabilities to support subsequent network security analysis.

[0031] In order to ensure the accuracy and reliability of the analysis results, the analysis and processing module will conduct strict quality control on the data during data extraction and transmission to ensure the integrity, accuracy and timeliness of the data. The pre-processing unit is used to remove noise and redundant information to ensure the neatness and consistency of the data. The online processing unit can quickly process massive data through efficient data processing technology and algorithms to meet the real-time requirements of network security analysis.

[0032] The assessment module can evaluate the overall security situation of the network based on the collected network traffic data, system logs, user behavior data, etc., and judge the security of the network system by analyzing abnormal traffic, malicious behavior, potential threats, etc. in the network, providing a basis for the formulation and adjustment of security policies. The assessment module can conduct quantitative assessments of different types of network security risks and provide specific risk levels and degrees of harm, which will help security managers understand the severity of the risks and formulate targeted preventive measures accordingly. By using advanced technologies such as big data processing, machine learning and data mining, the assessment module can achieve comprehensive and in-depth assessments and risk predictions of network security, providing strong support for the formulation and adjustment of security policies.

[0033] The big data storage module plays a vital role in the network security analysis system. It is responsible for efficiently storing and managing a large amount of network security data. These data include but are not limited to network traffic logs, system logs, user behavior logs, etc. They are the basis for security analysis. According to the content of network security analysis, the data is automatically stored or regularly cleaned up. The storage content and cleanup content depend on the use time of the computer equipment of big data.

[0034] The identity authentication module includes a face recognition unit and an account and password login unit. The face recognition unit is used to capture the user's facial image and verify the user's identity after comparison, and the account and password login unit is used to enter the correct account and password to verify the identity. The face recognition module verifies the user's identity by capturing the user's facial image and using advanced face recognition algorithms to compare it with the face template stored in the database. This unit not only improves the convenience of identity authentication, but also enhances the security of the system and effectively prevents security threats such as identity fraud. The account and password login module is an important entry point for users to enter the system. It combines the traditional account and password login method with big data technology for security enhancement, allowing users to enter the system by entering the correct account and password to verify their identity. This module not only requires users to enter the correct credential information, but may also combine big data technology to monitor and analyze user login behavior to identify and prevent potential security threats.

[0035] The data acquisition module includes a text acquisition unit and an image acquisition unit. The text acquisition unit is used to collect data from the file system, and the image acquisition unit is used to capture image data in the network environment. As an important submodule of the data acquisition module, the text acquisition unit focuses on collecting data from the file system. The file acquisition unit usually accesses and traverses the file system through the file system interface provided by the operating system. These interfaces allow programs to read file directories, file attributes, and file contents. In order to improve the acquisition efficiency, the file acquisition unit may support concurrent acquisition, that is, collecting data from multiple directories or files at the same time. During the acquisition process, the file acquisition unit needs to ensure the security and integrity of the data to prevent data leakage or tampering. This may require the implementation of security mechanisms such as encrypted transmission and access control.

[0036] In most cases, network security analysis systems focus on text-based data sources such as network traffic and system logs. However, with the continuous development of technology and the expansion of application scenarios, the role of image acquisition units in network security analysis may gradually increase. Image acquisition units capture image data in the network environment in real time through network cameras, sensors or other image acquisition devices, and analyze image data in combination with image recognition technology. They can detect possible abnormal behaviors or security threats, such as intruders and sabotage behaviors. By integrating image data with other network security data sources, a comprehensive security analysis view can be formed.

[0037] The data extraction module includes a desensitizing unit and a screening unit. The desensitizing unit is used to desensitize sensitive information during the data extraction process, and the screening unit is used to identify and screen the data to be extracted according to preset rules and conditions. In order to ensure the accuracy and reliability of the analysis results, the analysis and processing module will strictly monitor the quality of the data during data extraction and transmission to ensure the integrity, accuracy and timeliness of the data. The preprocessing unit is used to remove noise and redundant information to ensure the neatness and consistency of the data. During the data transmission process, the analysis unit will use various technical means, such as data encryption and access control, to ensure the security and confidentiality of the data during transmission. During the data processing and analysis process, the analysis unit will always pay attention to the quality of the data, and take effective measures to solve and optimize it to ensure the integrity, accuracy and timeliness of the data. This is an important basis for ensuring the accuracy and reliability of network security analysis.

[0038] The analysis and processing module includes a preprocessing unit and an online processing unit. The preprocessing unit is used to remove noise and redundant information in the data, and the online processing unit is used to perform online processing and analysis on the extracted data to identify potential threats and abnormal behaviors in the network. During the data transmission process, the analysis unit will use various technical means, such as data encryption and access control, to ensure the security and confidentiality of the data during the transmission process. During the data processing and analysis process, the analysis unit will always pay attention to the quality of the data, and take effective measures to solve and optimize it to ensure the integrity, accuracy and timeliness of the data, which is an important basis for ensuring the accuracy and reliability of network security analysis. The online processing unit adopts efficient data processing technology and algorithms, which can quickly process massive data and meet the real-time requirements of network security analysis. Its system architecture design is flexible and supports the addition and expansion of functional modules. The functions and performance of the processing unit can be adjusted according to actual needs. By using advanced algorithms and technical means (such as Choquet fuzzy integral fusion algorithm), the fault tolerance of the entire system is enhanced, and the accuracy and reliability of the analysis are improved. During the data processing and analysis process, the data privacy and security protection principles are strictly followed to ensure the security of data during transmission, storage and processing.

[0039] The evaluation module includes an early warning unit and a feedback unit. The early warning unit is used to issue early warning information to notify relevant personnel to take countermeasures, and the feedback unit is used to classify, filter and prioritize the early warning information. By using advanced technologies such as big data processing, machine learning and data mining, the evaluation module can achieve a comprehensive and in-depth assessment and risk prediction of network security, and provide strong support for the formulation and adjustment of security strategies. Once a potential security threat is discovered, the early warning unit will issue early warning information in a timely manner to notify relevant personnel to take countermeasures and reduce security risks. The feedback unit is used to classify, filter and prioritize the alarm information, and timely notify relevant personnel to handle it according to the urgency and importance of the alarm information, ensuring rapid response and effective disposal of security incidents.

[0040] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0041] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A network security analysis system based on big data, characterized by: The network security analysis system includes an identity authentication module, a data acquisition module, a data extraction module, an analysis and processing module, an evaluation module and a big data storage module; The identity verification module is used to accurately verify the user's identity, thereby effectively preventing unauthorized access and data leakage; The data acquisition module is used to collect various data in the network to provide a basis for subsequent security analysis; The data extraction module is used to efficiently extract and integrate data from various data sources for subsequent analysis; The analysis and processing module is used to perform in-depth analysis and processing on the data extracted and transmitted from various data sources; The evaluation module is used to evaluate the security situation and predict the risks of the processed data; The big data storage module is responsible for efficiently storing and managing a large amount of network security data.

2. A network security analysis system based on big data according to claim 1, characterized in that: The identity authentication module includes a face recognition unit and an account and password login unit. The face recognition unit is used to capture the user's facial image and verify the user's identity after comparison. The account and password login unit is used to enter the correct account number and password to verify the identity.

3. A network security analysis system based on big data according to claim 1, characterized in that: The data acquisition module includes a text acquisition unit and an image acquisition unit. The text acquisition unit is used to acquire data from a file system, and the image acquisition unit is used to capture image data in a network environment.

4. A network security analysis system based on big data according to claim 1, characterized in that: The data extraction module includes a desensitizing unit and a screening unit. The desensitizing unit is used to desensitize sensitive information during the data extraction process, and the screening unit is used to identify and screen the data to be extracted according to preset rules and conditions.

5. The network security analysis system based on big data according to claim 1 is characterized by: The analysis and processing module includes a preprocessing unit and an online processing unit. The preprocessing unit is used to remove noise and redundant information in the data, and the online processing unit is used to perform online processing and analysis on the extracted data to identify potential threats and abnormal behaviors in the network.

6. A network security analysis system based on big data according to claim 1, characterized in that: The evaluation module includes an early warning unit and a feedback unit. The early warning unit is used to issue early warning information to notify relevant personnel to take countermeasures, and the feedback unit is used to classify, filter and prioritize the early warning information.

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