CN115987695B - Network security monitoring system based on big data analysis - Google Patents
Network security monitoring system based on big data analysis Download PDFInfo
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- CN115987695B CN115987695B CN202310274391.5A CN202310274391A CN115987695B CN 115987695 B CN115987695 B CN 115987695B CN 202310274391 A CN202310274391 A CN 202310274391A CN 115987695 B CN115987695 B CN 115987695B
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention discloses a network security monitoring system based on big data analysis, which relates to the technical field of network security and comprises a data receiving module, a data classifying module, a security monitoring module, a distributed database and a data backup module; the data receiving module is used for collecting network information, network data and network content uploaded or edited by different IPs to obtain IP editing data; after receiving the IP editing data, the cloud server utilizes the data classification module to carry out monitoring coefficient analysis on the cached IP editing data so as to generate a monitoring priority table of the IP editing data; the data monitoring efficiency is improved; the safety monitoring module is used for judging whether the IP editing data has network danger or not; the data backup module is used for backing up the IP editing data without danger; selecting a storage block with the largest free coefficient as a selected block; the method is convenient for staff and other network users to check; the storage pressure is effectively reduced, and the data storage efficiency is improved.
Description
Technical Field
The invention relates to the technical field of network security, in particular to a network security monitoring system based on big data analysis.
Background
With the rapid development and deep application of computer science and technology, the revolution in network space is continuously changing and affecting people's life style; as people have a higher and higher dependence on the internet, and many secret information about enterprises and individuals are related on the internet, the problem of network security has been an important issue in the process of technical development.
From the perspective of network operation and manager, the operations such as access, read-write and the like of local network information are expected to be protected and controlled, and threats such as trapdoor, virus, illegal access, refused service, illegal occupation of network resources, illegal control and the like are avoided, so that attack of network hackers is prevented and defended; therefore, the invention provides a network security monitoring system based on big data analysis.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a network security monitoring system based on big data analysis.
To achieve the above object, an embodiment according to a first aspect of the present invention provides a network security monitoring system based on big data analysis, including a data receiving module, a data classifying module, a security monitoring module, a distributed database, and a data backup module;
the data receiving module is used for collecting network information, network data and network content uploaded or edited by different IPs, obtaining IP editing data and transmitting the IP editing data to the cloud server for caching;
after the cloud server receives the IP editing data, a data classification module is utilized to carry out monitoring coefficient JC analysis on the cached IP editing data, and a monitoring priority table of the IP editing data is generated; transmitting the IP editing data to a safety monitoring module in sequence according to the monitoring priority table for safety monitoring;
the safety monitoring module is used for monitoring network data, network information and network content in the IP editing data, then calculating the monitoring data according to memory calculation and real-time stream calculation, and judging whether the IP editing data has network danger or not;
if the danger exists, the early warning module is started to perform early warning; if no danger exists, the security monitoring module is used for transmitting the IP editing data to a distributed database;
the distributed database analyzes, compares, previews and presents the IP editing data, and judges whether the IP editing data has network danger or not again; if no danger exists, uploading and backing up the IP editing data;
the data backup module is used for backing up the IP editing data without danger; the data backup module comprises a plurality of storage blocks, and the storage block with the largest spare coefficient KY is selected as a selected block; and backing up the received IP editing data to the selected block.
Further, the specific analysis steps of the data classification module are as follows:
acquiring the data volume of IP editing data as LZ; acquiring an IP identity corresponding to the IP editing data; the IP identity is used for calling the data of the corresponding IP and uploading and recording; the data uploading record comprises data uploading time, uploading data quantity and whether uploading is successful or not;
performing threat value WX evaluation according to the data uploading record; calculating a monitoring coefficient JC of the IP editing data by using a formula JC=LZ×b1+WX×b2, wherein b1 and b2 are scale factors; and sequencing the IP editing data according to the magnitude of the monitoring coefficient JC, generating a monitoring priority table of the IP editing data, and feeding back the monitoring priority table to the cloud server.
Further, threat value WX evaluation is performed according to the data uploading record, specifically:
counting the total uploading times of the corresponding IP as the editing frequency P1 in a preset time period;
counting the upload failure ratio of the corresponding IP as Zb; intercepting the time period between adjacent uploading failures as an alarm buffer time period, and counting the uploading times of the corresponding IP in each alarm buffer time period as alarm buffer frequency Li; comparing the warning buffer frequency Li with a preset buffer threshold;
counting the times of Li smaller than a preset buffer threshold as C1; when Li is smaller than a preset buffer threshold value, obtaining a difference value between Li and the preset buffer threshold value, and summing to obtain a difference total value GZ; calculating by using a formula CJ=C1×a3+GZ×a4 to obtain a differential attraction value CJ, wherein a3 and a4 are scale factors;
normalizing the editing frequency, the uploading failure duty ratio and the slow attraction value, taking the numerical value, and calculating by using a formula WX=eta× (Zb×a1+CJ×a2)/P1 to obtain a threat value WX of the IP, wherein a1 and a2 are both scale factors; η is a preset compensation factor.
Further, the data backup module specifically includes:
obtaining the residual memory data of the memory block and marking the residual memory data as Nc; establishing a graph of the change of the remaining memory data Nc along with time, and deriving the graph to obtain a memory change rate graph;
marking the memory change rate as NBi; comparing NBi with a preset rate threshold; if NBi is more than or equal to a preset rate threshold, a corresponding curve segment is intercepted in a corresponding curve graph and marked;
in a preset time period, counting the number of marked curve segments to be N1; integrating the time of the difference value between the corresponding NBi on all the marked curve segments and a preset speed threshold to obtain a marked reference area M1;
calculating to obtain a memory change index Cs by using a formula Cs=N1×r3+M1×r4, wherein r3 and r4 are coefficient factors; and obtaining the spare coefficient KY of the storage block by using a formula KY= (Nc×r5)/(Cs×r6), wherein r5 and r6 are coefficient factors.
Further, the safety monitoring module specifically includes:
the memory calculation uses a Spark framework to realize memory-based data calculation;
the real-time stream calculation is used for receiving and calculating the data of the data collection unit in real time, realizing the processing of cleaning and analyzing the data through a calculation service, and outputting the result to a distributed database; wherein the real-time stream computation adopts a Storm framework, singly adopts any one or the combination of the two of Spark frameworks.
Further, the distributed database comprises an analysis unit, a comparison unit, a previewing unit and a presentation unit; the analysis unit is used for carrying out classification analysis on the received IP editing data;
the comparison unit is used for comparing the IP editing data with the data in the distributed database;
the previewing unit is used for previewing the IP editing data content according to the comparison data;
the presentation unit is used for presenting the previewing result and judging whether the IP editing data has network danger or not again.
Further, when the early warning module is triggered, the audible and visual alarm starts to give out buzzing and light, informs staff to check, locks the uploading IP and alerts the uploading IP to stop uploading or editing actions.
Compared with the prior art, the invention has the beneficial effects that:
the data receiving module is used for collecting network information, network data and network content uploaded or edited by different IPs to obtain IP editing data; after the cloud server receives the IP editing data, a data classification module is utilized to carry out monitoring coefficient JC analysis on the cached IP editing data, and a monitoring priority table of the IP editing data is generated; the cloud server is used for sequentially transmitting the corresponding IP editing data to the safety monitoring module for safety monitoring according to the monitoring priority table; the data monitoring efficiency is improved;
the safety monitoring module is used for monitoring network data, network information and network content in the IP editing data, then calculating the monitoring data according to memory calculation and real-time stream calculation, and judging whether the IP editing data has network danger or not; if the danger exists, the early warning module is started to perform early warning; locking the uploading IP, and warning the uploading IP to stop uploading or editing actions; the uploading person is conveniently and timely found by later network security maintenance personnel, so that the network security is improved; if no danger exists, transmitting the IP editing data to a distributed database; the distributed database analyzes, compares, previews and presents the IP editing data, judges whether the IP editing data has network danger again, and uploads and backs up the IP editing data if the IP editing data has no network danger; the method is convenient for staff and other network users to check;
the data backup module is used for backing up the IP editing data without danger; performing spare coefficient analysis on a plurality of storage blocks, selecting the storage block with the largest spare coefficient KY as a selected block, and backing up the received IP editing data to the selected block; the invention can reasonably select the corresponding storage blocks to store data according to the storage conditions of the storage blocks, effectively lighten the storage pressure of a computer and improve the data storage efficiency.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a network security monitoring system based on big data analysis according to the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a network security monitoring system based on big data analysis includes a data receiving module, a cloud server, a data classifying module, a security monitoring module, an early warning module, a distributed database and a data backup module;
the data receiving module is used for collecting network information, network data and network content uploaded or edited by different IPs to obtain IP editing data; transmitting the IP editing data to a cloud server for caching;
after receiving the IP editing data, the cloud server carries out monitoring coefficient JC analysis on the cached IP editing data by utilizing a data classification module to generate a monitoring priority table of the IP editing data; the specific analysis steps of the data classification module are as follows:
acquiring the data volume of IP editing data as LZ; acquiring an IP identity corresponding to the IP editing data; the IP identity is called to upload and record the data corresponding to the IP; the data uploading record comprises the time of uploading the data, the data uploading amount and whether the uploading is successful or not; if the uploading fails, the uploading data is represented to have network danger, and the uploading IP is warned;
the threat value WX is evaluated according to the data uploading record, and the method specifically comprises the following steps:
counting the total uploading times of the corresponding IP as the editing frequency P1 in a preset time period;
counting the upload failure ratio of the corresponding IP as Zb; intercepting the time period between adjacent uploading failures as an alarm buffer time period, and counting the uploading times of the corresponding IP in each alarm buffer time period as alarm buffer frequency Li; comparing the warning buffer frequency Li with a preset buffer threshold;
counting the times of Li smaller than a preset buffer threshold as C1; when Li is smaller than a preset buffer threshold value, obtaining a difference value between Li and the preset buffer threshold value, and summing to obtain a difference total value GZ; calculating by using a formula CJ=C1×a3+GZ×a4 to obtain a differential attraction value CJ, wherein a3 and a4 are scale factors;
normalizing the editing frequency, the uploading failure duty ratio and the slow attraction value, taking the numerical value, and calculating by using a formula WX=eta× (Zb×a1+CJ×a2)/P1 to obtain a threat value WX of the IP, wherein a1 and a2 are both scale factors; η is a preset compensation factor;
calculating a monitoring coefficient JC of the IP editing data by using a formula JC=LZ×b1+WX×b2, wherein b1 and b2 are scale factors;
sorting the IP editing data according to the magnitude of the monitoring coefficient JC to generate a monitoring priority table of the IP editing data; the data classification module is used for feeding back a monitoring priority table of the IP editing data to the cloud server; the cloud server is used for sequentially transmitting the corresponding IP editing data to the safety monitoring module for safety monitoring according to the monitoring priority table; the data monitoring efficiency is improved;
the safety monitoring module is used for monitoring network data, network information and network content in the IP editing data, then calculating the monitoring data according to memory calculation and real-time stream calculation, and judging whether the IP editing data has network danger or not;
if the danger exists, the early warning module is started to perform early warning; if no danger exists, the security monitoring module is used for transmitting the IP editing data to the distributed database;
the memory calculation uses a Spark framework to realize memory-based data calculation;
the real-time flow calculation is used for receiving and calculating the data of the data collection unit in real time, realizing the processing of cleaning and analyzing the data through the calculation service, and outputting the result to the distributed database;
wherein, the real-time stream calculation adopts a Storm framework, singly adopts any one of Spark frameworks or the combination of the two frameworks; the flow calculation mode can well analyze the large-scale flow data in real time in the continuously-changing motion process, capture possibly useful information and send the result to the next calculation node;
the distributed database comprises an analysis unit, a comparison unit, a previewing unit and a presentation unit; the analysis unit is used for carrying out classification analysis on the received IP editing data; the comparison unit is used for comparing the IP editing data with the data in the distributed database; the previewing unit is used for previewing the IP editing data content according to the comparison data; the presentation unit is used for presenting the previewing result and judging whether the IP editing data have network danger or not again;
if the danger exists, the early warning module is started to perform early warning;
if no danger exists, uploading and backing up the IP editing data; the method is convenient for staff and other network users to check;
when the early warning module is triggered, the audible and visual alarm is started to give out buzzes and lights, and can inform workers to check and lock the uploading IP, and the uploading IP is warned to stop uploading or editing actions; the uploading person is conveniently and timely found by later network security maintenance personnel, so that the network security is improved;
the data backup module is used for backing up the IP editing data without danger; the data backup module comprises a plurality of storage blocks, and the specific backup steps are as follows:
obtaining the residual memory data of the memory block and marking the residual memory data as Nc; establishing a graph of the change of the remaining memory data Nc along with time, and deriving the graph to obtain a memory change rate graph;
marking the memory change rate as NBi; comparing NBi with a preset rate threshold; if NBi is more than or equal to a preset rate threshold, a corresponding curve segment is intercepted in a corresponding curve graph and marked;
in a preset time period, counting the number of marked curve segments to be N1; integrating the time of the difference value between the corresponding NBi on all the marked curve segments and a preset speed threshold to obtain a marked reference area M1;
calculating to obtain a memory change index Cs by using a formula Cs=N1×r3+M1×r4, wherein r3 and r4 are coefficient factors; normalizing the residual memory data and the memory change index, taking the values of the residual memory data and the memory change index, and calculating by using a formula KY= (Nc×r5)/(Cs×r6) to obtain a spare coefficient KY of the memory block, wherein r5 and r6 are coefficient factors;
selecting a storage block with the largest spare coefficient KY as a selected block; the data backup module is used for backing up the received IP editing data to the selected block; the invention can reasonably select the corresponding storage blocks to store data according to the storage conditions of the storage blocks, effectively lighten the storage pressure of a computer and improve the data storage efficiency.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The working principle of the invention is as follows:
the network safety monitoring system based on big data analysis is characterized in that when in operation, a data receiving module is used for collecting network information, network data and network content uploaded or edited by different IPs to obtain IP editing data; transmitting the IP editing data to a cloud server for caching; after receiving the IP editing data, the cloud server carries out monitoring coefficient JC analysis on the cached IP editing data by utilizing a data classification module to generate a monitoring priority table of the IP editing data; the cloud server is used for sequentially transmitting the corresponding IP editing data to the safety monitoring module for safety monitoring according to the monitoring priority table; the data monitoring efficiency is improved;
the safety monitoring module is used for monitoring network data, network information and network content in the IP editing data, then calculating the monitoring data according to memory calculation and real-time stream calculation, and judging whether the IP editing data has network danger or not; if the danger exists, the early warning module is started to perform early warning; locking the uploading IP, and warning the uploading IP to stop uploading or editing actions; the uploading person is conveniently and timely found by later network security maintenance personnel, so that the network security is improved; if no danger exists, the IP editing data is transmitted to a distributed database; the distributed database analyzes, compares, previews and presents the IP editing data, judges whether the IP editing data has network danger again, and uploads and backs up the IP editing data if the IP editing data has no network danger; the method is convenient for staff and other network users to check;
the data backup module is used for backing up the IP editing data without danger; performing spare coefficient analysis on a plurality of storage blocks, selecting the storage block with the largest spare coefficient KY as a selected block, and backing up the received IP editing data to the selected block; the invention can reasonably select the corresponding storage blocks to store data according to the storage conditions of the storage blocks, effectively lighten the storage pressure of a computer and improve the data storage efficiency.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (4)
1. The network safety monitoring system based on big data analysis is characterized by comprising a data receiving module, a cloud server, a data classifying module, a safety monitoring module, an early warning module, a distributed database and a data backup module;
the data receiving module is used for collecting network information, network data and network content uploaded or edited by different IPs, obtaining IP editing data and transmitting the IP editing data to the cloud server for caching;
after the cloud server receives the IP editing data, a data classification module is utilized to carry out monitoring coefficient JC analysis on the cached IP editing data; the specific analysis steps of the data classification module are as follows:
acquiring the data volume of IP editing data as LZ; acquiring an IP identity corresponding to the IP editing data; the IP identity is used for calling the data of the corresponding IP and uploading and recording; the data uploading record comprises data uploading time, uploading data quantity and whether uploading is successful or not;
counting the total uploading times of the corresponding IP as the editing frequency P1 in a preset time period;
counting the upload failure ratio of the corresponding IP as Zb; intercepting the time period between adjacent uploading failures as an alarm buffer time period, and counting the uploading times of the corresponding IP in each alarm buffer time period as alarm buffer frequency Li; comparing the warning buffer frequency Li with a preset buffer threshold;
counting the times of Li smaller than a preset buffer threshold as C1; when Li is smaller than a preset buffer threshold value, obtaining a difference value between Li and the preset buffer threshold value, and summing to obtain a difference total value GZ; calculating by using a formula CJ=C1×a3+GZ×a4 to obtain a differential attraction value CJ, wherein a3 and a4 are scale factors;
normalizing the editing frequency, the uploading failure duty ratio and the slow attraction value, taking the numerical value, and calculating by using a formula WX=eta× (Zb×a1+CJ×a2)/P1 to obtain a threat value WX of the IP, wherein a1 and a2 are both scale factors; η is a preset compensation factor;
calculating a monitoring coefficient JC of the IP editing data by using a formula JC=LZ×b1+WX×b2, wherein b1 and b2 are scale factors; sorting the IP editing data according to the magnitude of the monitoring coefficient JC to generate a monitoring priority table of the IP editing data; the monitoring priority table is fed back to a cloud server; the cloud server is used for sequentially transmitting the IP editing data to the safety monitoring module for safety monitoring according to the monitoring priority table;
the safety monitoring module is used for monitoring network data, network information and network content in the IP editing data, then calculating the monitoring data according to memory calculation and real-time stream calculation, and judging whether the IP editing data has network danger or not;
if the danger exists, the early warning module is started to perform early warning; if no danger exists, the security monitoring module is used for transmitting the IP editing data to a distributed database;
the distributed database analyzes, compares, previews and presents the IP editing data, and judges whether the IP editing data has network danger or not again; if no danger exists, uploading and backing up the IP editing data;
the data backup module is used for backing up the IP editing data without danger; the data backup module comprises a plurality of storage blocks, and the specific backup steps are as follows:
obtaining the residual memory data of the memory block and marking the residual memory data as Nc; establishing a graph of the change of the remaining memory data Nc along with time, and deriving the graph to obtain a memory change rate graph;
marking the memory change rate as NBi; comparing NBi with a preset rate threshold; if NBi is more than or equal to a preset rate threshold, a corresponding curve segment is intercepted in a corresponding curve graph and marked;
in a preset time period, counting the number of marked curve segments to be N1; integrating the time of the difference value between the corresponding NBi on all the marked curve segments and a preset speed threshold to obtain a marked reference area M1;
calculating to obtain a memory change index Cs by using a formula Cs=N1×r3+M1×r4, wherein r3 and r4 are coefficient factors; obtaining a spare coefficient KY of the storage block by using a formula KY= (Nc×r5)/(Cs×r6), wherein r5 and r6 are coefficient factors;
and selecting the storage block with the largest spare coefficient KY as a selected block, and backing up the received IP editing data to the selected block.
2. The network security monitoring system based on big data analysis of claim 1, wherein the security monitoring module specifically comprises:
the memory calculation uses a Spark framework to realize memory-based data calculation;
the real-time stream calculation is used for receiving and calculating the data of the data collection unit in real time, realizing the processing of cleaning and analyzing the data through a calculation service, and outputting the result to a distributed database; wherein the real-time stream computation adopts a Storm framework, singly adopts any one or the combination of the two of Spark frameworks.
3. The network security monitoring system based on big data analysis of claim 1, wherein the distributed database comprises an analysis unit, a comparison unit, a previewing unit and a presentation unit; the analysis unit is used for carrying out classification analysis on the received IP editing data;
the comparison unit is used for comparing the IP editing data with the data in the distributed database;
the previewing unit is used for previewing the IP editing data content according to the comparison data;
the presentation unit is used for presenting the previewing result and judging whether the IP editing data has network danger or not again.
4. The network security monitoring system based on big data analysis of claim 1, wherein when the early warning module is triggered, the audible and visual alarm is started to give out buzzing and light, inform staff to check and lock the uploading IP, and warn the uploading IP to stop uploading or editing actions.
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