Network security early warning system based on knowledge graph

Abstract

The invention relates to the technical field of network security, in particular to a network security early warning system based on a knowledge graph, and solves the problems that in the prior art, the number of network security early warning systems based on the knowledge graph is still small, the early warning accuracy is not high, and the real-time performance is not high. A network security early warning system based on a knowledge graph comprises a data preprocessing and knowledge extraction module, a knowledge graph construction and storage module, a threat detection and early warning analysis module and an adaptive learning and updating module. According to the method, the early warning accuracy is improved, and the system can more deeply understand the relationship and the attack mode between the network entities by constructing the accurate and comprehensive network security knowledge graph, so that the early warning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of network security technology, and in particular to a network security early warning system based on a knowledge graph. Background Art

[0002] In today's highly information-based society, network security issues have become a global challenge. With the continuous advancement of network technology, network attack methods have become increasingly complex and hidden, making it difficult for traditional security protection measures to effectively respond. Therefore, it is particularly important to develop a system that can detect and warn of network threats in real time and accurately. As an emerging data structure, knowledge graphs can display the relationship between entities in a graphical way, providing a new perspective and method for network security analysis. However, there are still few network security early warning systems based on knowledge graphs on the market, and there are problems such as low accuracy of early warnings and low real-time performance. In order to solve these problems, the present invention proposes a network security early warning system based on knowledge graphs. Summary of the invention

[0003] The purpose of the present invention is to provide a network security early warning system based on knowledge graph, which solves the problem that there are still few network security early warning systems based on knowledge graph in the prior art, and there are problems such as low early warning accuracy and poor real-time performance.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A network security early warning system based on knowledge graph includes a data preprocessing and knowledge extraction module, a knowledge graph construction and storage module, a threat detection and early warning analysis module, and an adaptive learning and updating module.

[0006] Preferably, the data preprocessing and knowledge extraction module is responsible for collecting, cleaning and integrating network security related data, and extracting key information for subsequent processing.

[0007] Preferably, the knowledge graph construction and storage module uses graph database technology to construct a network security knowledge graph, and supports query and optimization functions of the graph.

[0008] Preferably, the threat detection and early warning analysis module uses a graph analysis algorithm to monitor abnormal behaviors in the network in real time, and triggers an early warning mechanism when an anomaly is found.

[0009] Preferably, the adaptive learning and updating module uses machine learning technology to train and learn historical warning data, continuously optimizes the warning model, and supports both manual and automatic updating methods.

[0010] Preferably, the system provides a visual network security warning interface, displays warning information, network entity relationships and abnormal behavior analysis results, and supports access and expansion functions of multiple data sources.

[0011] Preferably, the modules of the system transmit and interact with each other via standardized interfaces to ensure the stability and scalability of the system. At the same time, the system also supports custom rules and data filtering functions to meet the needs of different users.

[0012] The present invention has at least the following beneficial effects:

[0013] Improve early warning accuracy: By building an accurate and comprehensive network security knowledge graph, the system can gain a deeper understanding of the relationships between network entities and attack patterns, thereby improving the accuracy of early warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0015] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] Embodiment 1, refer to Figure 1 ,A network security early warning system based on knowledge graph, includes a data preprocessing and knowledge extraction module, a knowledge graph construction and storage module, a threat detection and early warning analysis module, and an adaptive learning and updating module.

[0018] The system includes the following core modules:

[0019] 1. Data preprocessing and knowledge extraction module:

[0020] This module is mainly responsible for collecting, cleaning and integrating network security data from different sources, such as logs, alarms, traffic data, etc. It uses natural language processing, regular expressions and other technical means to extract key information from the raw data, such as IP addresses, port numbers, attack types, etc., to provide a high-quality data foundation for the subsequent knowledge graph construction.

[0021] 2. Knowledge graph construction and storage module:

[0022] This module uses graph database technology to store the extracted key information in the form of nodes and edges to build a network security knowledge graph. The nodes in the graph represent network entities (such as hosts, devices, services, etc.), and the edges represent the relationships between entities (such as connections, access, attacks, etc.). By continuously optimizing the graph structure and algorithm, the accuracy and query efficiency of the graph are improved.

[0023] 3. Threat detection and early warning analysis module:

[0024] Based on the constructed knowledge graph, this module uses graph analysis algorithms (such as shortest path, community discovery, etc.) to monitor abnormal behaviors in the network in real time. By comparing historical data with current data, potential threats and attack patterns, such as DDoS attacks and botnets, are identified. Once an abnormality is found, the system will immediately trigger the early warning mechanism and notify the administrator through email, SMS, etc. for disposal.

[0025] 4. Adaptive learning and updating module:

[0026] In order to adapt to the ever-changing network security environment, this module uses machine learning technology to train and learn historical warning data and continuously optimize the warning model. At the same time, the system also supports manual and automatic updates to ensure the timeliness and accuracy of the knowledge graph and warning model.

[0027] According to the above embodiments, it can be seen that: Improving the accuracy of early warning: By constructing an accurate and comprehensive network security knowledge graph, the system can have a deeper understanding of the relationships and attack patterns between network entities, thereby improving the accuracy of early warning.

[0028] Enhanced real-time performance: The system can monitor abnormal behaviors in the network in real time and issue early warnings, effectively shortening the time interval from the occurrence of an attack to its discovery and reducing losses.

[0029] Improve scalability and adaptability: Through adaptive learning and updating modules, the system can continuously learn and adapt to new network environments to maintain the effectiveness and accuracy of the early warning model. At the same time, the system also supports the access and expansion of multiple data sources to improve the comprehensiveness of early warning.

[0030] Example 2, refer to Figure 1 , where the data preprocessing and knowledge extraction module is responsible for collecting, cleaning and integrating network security related data, and extracting key information for subsequent processing.

[0031] The knowledge graph construction and storage module uses graph database technology to build a network security knowledge graph, and supports graph query and optimization functions.

[0032] The threat detection and early warning analysis module uses graph analysis algorithms to monitor abnormal behaviors in the network in real time and triggers an early warning mechanism when an anomaly is found.

[0033] The adaptive learning and update module uses machine learning technology to train and learn historical warning data, continuously optimizes the warning model, and supports both manual and automatic update methods.

[0034] The system provides a visual network security warning interface that displays warning information, network entity relationships, and abnormal behavior analysis results, and supports access and expansion functions from multiple data sources.

[0035] The various modules of the system transmit and interact with each other through standardized interfaces to ensure the stability and scalability of the system. At the same time, the system also supports custom rules and data filtering functions to meet the needs of different users.

[0036] 1. Detailed description of system architecture and workflow:

[0037] The network security early warning system based on knowledge graph proposed in the present invention adopts a distributed architecture and supports high concurrent processing and massive data storage. The system first collects and processes network security related data through the data preprocessing and knowledge extraction module, and then constructs the network security knowledge graph using the knowledge graph construction and storage module. On this basis, the threat detection and early warning analysis module monitors abnormal behaviors in the network in real time and issues early warnings. Finally, the adaptive learning and update module is responsible for updating and optimizing the knowledge graph and early warning model. The entire system forms a closed loop, constantly learning and evolving to adapt to the ever-changing network security environment.

[0038] 2. Detailed description of the functions and technical implementation of each module:

[0039] Data preprocessing and knowledge extraction module: describes in detail the source of data, format conversion, cleaning and integration process, as well as the extraction methods and technical means of key information. This module also supports custom rules and data filtering functions to meet the needs of different users.

[0040] Knowledge graph construction and storage module: This module specifically introduces the selection and configuration of graph databases, graph construction algorithms and storage structures, as well as graph query and optimization techniques. This module also supports a variety of graph visualization tools to facilitate users to intuitively view and analyze graph information.

[0041] Threat detection and early warning analysis module: This module explains the principles and implementation process of graph analysis algorithms, including shortest path analysis, community discovery, etc. At the same time, this module also supports a variety of early warning trigger conditions and early warning information display methods, such as email notifications, SMS reminders, etc. In addition, it also provides the query and analysis function of early warning historical data to help users gain a deeper understanding of the trends and characteristics of network threats.

[0042] Adaptive learning and updating module: explains the selection and implementation process of machine learning algorithms, including data set preparation, model training and evaluation, etc. This module also supports manual and automatic updates to ensure the timeliness and accuracy of knowledge graphs and early warning models. At the same time, users can also customize update strategies and model parameters according to actual needs to meet different application scenarios.

[0043] According to the above embodiments, the knowledge graph-based network security early warning system provided by the present invention realizes comprehensive monitoring and intelligent early warning of network security by comprehensively applying multiple modules such as data preprocessing, knowledge graph construction, threat detection and early warning, and adaptive learning.

[0044] Comprehensive security protection: The system builds a comprehensive and detailed security protection network by collecting and analyzing multi-source network security data. From data preprocessing to knowledge graph construction, to threat detection and early warning analysis, each link is closely connected to ensure real-time monitoring and rapid response to network security conditions.

[0045] Efficient threat detection and early warning: By using knowledge graph technology and graph analysis algorithms, the system can quickly identify abnormal behaviors and potential threats in the network and achieve efficient threat detection. At the same time, once an abnormality is detected, the system will immediately trigger the early warning mechanism and notify the administrator in time to deal with it, thereby greatly reducing the losses caused by network attacks.

[0046] Powerful adaptive learning capabilities: The system not only has real-time monitoring and early warning functions, but also continuously learns and evolves from historical data through adaptive learning and update modules. This enables the system to continuously optimize the early warning model, improve the accuracy of early warnings, and effectively respond to the ever-changing network security environment.

[0047] High scalability and flexibility: The system adopts a distributed architecture and standardized interface design, supports the access and expansion of multiple data sources, and ensures the scalability of the system. At the same time, users can also customize rules and data filtering functions according to actual needs, making the system more flexible and meeting the security protection needs in different scenarios.

[0048] The knowledge graph-based network security early warning system of the present invention realizes all-round, efficient, adaptive and scalable network security protection by integrating multiple functional modules, providing an innovative and effective solution for the field of network security.

[0049] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A network security early warning system based on knowledge graph, characterized in that: include: Data preprocessing and knowledge extraction module, knowledge graph construction and storage module, threat detection and early warning analysis module, and adaptive learning and update module.

2. According to claim 1, a network security early warning system based on knowledge graph is characterized in that: The data preprocessing and knowledge extraction module is responsible for collecting, cleaning and integrating network security related data, and extracting key information for subsequent processing.

3. A network security early warning system based on knowledge graph according to claim 2, characterized in that: The knowledge graph construction and storage module uses graph database technology to build a network security knowledge graph, and supports graph query and optimization functions.

4. A network security early warning system based on knowledge graph according to claim 3, characterized in that: The threat detection and early warning analysis module uses graph analysis algorithms to monitor abnormal behaviors in the network in real time and triggers an early warning mechanism when an anomaly is found.

5. According to the knowledge graph-based network security early warning system of claim 1, it is characterized in that: The adaptive learning and update module uses machine learning technology to train and learn historical warning data, continuously optimizes the warning model, and supports both manual and automatic update methods.

6. According to the knowledge graph-based network security early warning system of claim 2, it is characterized in that: The system provides a visual network security warning interface that displays warning information, network entity relationships, and abnormal behavior analysis results, and supports access and expansion functions from multiple data sources.

7. A network security early warning system based on knowledge graph according to claim 6, characterized in that: The various modules of the system transmit and interact with each other through standardized interfaces to ensure the stability and scalability of the system. At the same time, the system also supports custom rules and data filtering functions to meet the needs of different users.

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