CN112491806A - Cloud platform flow security analysis system and method - Google Patents
Cloud platform flow security analysis system and method Download PDFInfo
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- CN112491806A CN112491806A CN202011214938.5A CN202011214938A CN112491806A CN 112491806 A CN112491806 A CN 112491806A CN 202011214938 A CN202011214938 A CN 202011214938A CN 112491806 A CN112491806 A CN 112491806A
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- 230000002159 abnormal effect Effects 0.000 claims abstract description 36
- 238000013528 artificial neural network Methods 0.000 claims abstract description 31
- 238000003062 neural network model Methods 0.000 claims abstract description 13
- 238000005206 flow analysis Methods 0.000 claims abstract description 4
- 230000010354 integration Effects 0.000 claims description 7
- 210000002569 neuron Anatomy 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 6
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- 238000013507 mapping Methods 0.000 abstract description 3
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1425—Traffic logging, e.g. anomaly detection
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1433—Vulnerability analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/20—Network architectures or network communication protocols for network security for managing network security; network security policies in general
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Abstract
The invention discloses a cloud platform flow security analysis system and a method, wherein the cloud platform flow security analysis system comprises: the flow acquisition unit is used for acquiring a network flow data packet in real time and storing the network flow data packet into a first preset database; the flow analysis unit is used for extracting the flow characteristics of the network flow packets stored in the first preset database and storing the flow characteristics into the second preset database; and the neural network unit analyzes and judges the traffic characteristics stored in the second preset database based on the plurality of BP neural networks to determine whether the network traffic data packet is an abnormal data packet. The invention obtains better effect than single optimal neural network by establishing a mode of integrating and outputting a plurality of BP neural network models, the BP neural network has strong nonlinear mapping capability and can be used for fitting complex functions, and the network flow safety analysis system established by the invention not only has high accuracy, but also has lower complexity and can be used on different terminals.
Description
Technical Field
The invention relates to the technical field of network security, in particular to a cloud platform traffic security analysis system and method.
Background
With the continuous increase of the scale of the internet, the cloud platform quantity is accurately and safely analyzed, the effective management of the operation state of the cloud platform network is favorably realized, the cloud platform network flow safety analysis is also an important premise for realizing the cloud platform network control and the cloud platform network planning and ensuring the cloud platform network safety with the service quality, and the cloud platform network flow safety analysis has important significance in the aspects of new generation network protocol design, network management and diagnosis, high-performance router design and the like.
Disclosure of Invention
The invention aims to provide a cloud platform flow security analysis system and a cloud platform flow security analysis method, so as to improve the accuracy and reduce the complexity.
In order to solve the above technical problem, the present invention provides a cloud platform traffic safety analysis system, including:
the flow acquisition unit is used for acquiring a network flow data packet in real time and storing the network flow data packet into a first preset database;
the flow analysis unit is used for extracting the flow characteristics of the network flow packets stored in the first preset database and storing the flow characteristics into the second preset database;
and the neural network unit analyzes and judges the traffic characteristics stored in the second preset database based on the plurality of BP neural networks to determine whether the network traffic data packet is an abnormal data packet.
Further, the cloud platform traffic safety analysis system further comprises a marking unit, which is used for marking the abnormal data packet when the network traffic data packet is judged to be the abnormal data packet, and storing the corresponding abnormal data packet into a third preset database.
Further, the flow characteristics include: the packet source IP address, the port number of the packet source address, the port number of the packet destination address, and the packet length.
Further, the cloud platform traffic safety analysis system further comprises a judgment module, a plurality of judgment rules for judging the risk level of the traffic packet are arranged in the judgment module, and the judgment rules include:
if the occurrence frequency of the IP address of the data packet source is within the range of the first frequency threshold, the network flow data packet is at high risk;
if the occurrence frequency of the IP address of the data packet source is within the range of the second frequency threshold, the network flow data packet is in danger;
if the occurrence frequency of the source IP address of the data packet is within the range of the third frequency threshold, the network flow data packet is low in risk;
and if the length of the data packet is not greater than the first length threshold value, the network flow data packet is at low risk, otherwise, the network flow data packet is at high risk.
Further, the determining rule further includes: and presetting an expression of an abnormal port number, wherein if the relationship between the port number of the source address of the data packet and the port number of the destination address of the data packet conforms to the expression of the abnormal port number, the network flow data packet is at a high risk, otherwise, the network flow data packet is at a low risk.
Further, the cloud platform traffic safety analysis system further comprises a fourth preset database, and an expression of a preset abnormal port number is stored in the fourth preset database.
Further, the cloud platform traffic safety analysis system further comprises an assignment module, wherein the assignment module is used for assigning values according to the risk level of the network traffic packet, the source IP address of the data packet, the port number of the source address of the data packet, the port number of the destination address of the data packet and the importance degree of the length of the data packet.
Further, the neural network unit comprises an input part, a neural network part and an integrated output part, wherein the input part, the neural network part and the integrated output part are sequentially connected by signals, and input factors of the input part are the risk level of the network traffic packet after assignment, the source IP address of the data packet, the port number of the source address of the data packet, the port number of the destination address of the data packet and the importance degree of the length of the data packet;
the neural network part is a BP neural network model with different hidden layer neuron numbers, and the range of the hidden layer neuron numbers is [6,10 ].
Further, the integration output part integrates output results of the plurality of BP neural network models by adopting a weighted average method, and the output result of the integration output part is a final network traffic packet abnormal condition risk index value.
The invention also provides a cloud platform flow security analysis method, which comprises the following steps:
s1, collecting network flow data packets in real time and storing the network flow data packets in a first preset database;
s2, extracting the traffic characteristics of the network traffic packet stored in the first preset database, and storing the traffic characteristics in the second preset database;
s3, judging the risk level of the traffic packet by adopting a plurality of judgment rules based on the traffic characteristics;
s4, assigning the risk level of the network traffic packet and the importance degree of the traffic characteristics;
s5, establishing a neural network part comprising a plurality of BP neural network models for learning and training, and integrating training results of the plurality of BP neural network models to obtain a network traffic packet abnormal condition risk index value based on neural network integration;
and S6, marking the network traffic packet with higher network traffic packet abnormal situation risk index value as an abnormal traffic packet.
The embodiment of the invention has the beneficial effects that: the BP neural network has strong nonlinear mapping capability and can be used for fitting complex functions.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of a cloud platform traffic security analysis system according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a neural network unit according to a first embodiment of the present invention.
Fig. 3 is a schematic flow chart of a cloud platform traffic security analysis method according to a second embodiment of the present invention.
Detailed Description
The following description of the embodiments refers to the accompanying drawings, which are included to illustrate specific embodiments in which the invention may be practiced.
Referring to fig. 1 to fig. 2, an embodiment of the present invention provides a cloud platform traffic safety analysis system, including:
the flow acquisition unit is used for acquiring a network flow data packet in real time and storing the network flow data packet into a first preset database;
the flow analysis unit is used for extracting the flow characteristics of the network flow packets stored in the first preset database and storing the flow characteristics into the second preset database;
and the neural network unit analyzes and judges the traffic characteristics stored in the second preset database based on the plurality of BP neural networks to determine whether the network traffic data packet is an abnormal data packet.
In the embodiment, the network traffic data is collected in real time and stored in the first preset database, so that the real-time collection and storage of the network traffic data are realized, data support is provided for a network traffic safety analysis system, whether the collected network traffic data is abnormal or not is determined according to the collected network traffic data in the preset time period, the efficiency of detecting the network traffic abnormality is improved, the abnormality analysis of unknown network traffic is realized, the accuracy of detecting the network traffic abnormality is improved, the method is suitable for diversified traffic types, and the real-time requirement of abnormality detection is met.
In any of the above embodiments, preferably, the flow characteristics include: the packet source IP address, the port number of the packet source address, the port number of the packet destination address, and the packet length.
In any of the foregoing embodiments, preferably, the system further includes a determining module, where a plurality of determining rules for determining the risk level of the traffic packet are set in the determining module, where the determining rules include:
if the occurrence frequency of the IP address of the data packet source is within the range of the first frequency threshold, the network flow data packet is at high risk;
the first frequency threshold value can be preferably set to be more than 70%, when the occurrence frequency of the IP address of the data packet source is more than 70%, the network traffic data packet is high risk;
and if the occurrence frequency of the IP address of the data packet source is within the second frequency threshold range, the network flow data packet is in danger.
The second frequency threshold value can be set to be more than 50% and less than 70%, when the occurrence frequency of the source IP address of the data packet is in the range of more than 50% and less than 70%, the network traffic data packet is in danger;
if the occurrence frequency of the source IP address of the data packet is within the range of the third frequency threshold, the network flow data packet is low in risk;
the third frequency threshold value can be preferably set to be less than 50%, when the occurrence frequency of the IP address of the data packet source is less than 50%, the network flow data packet is in danger;
if the length of the data packet is not larger than the first length threshold value, the network flow data packet is in low risk, otherwise, the network flow data packet is in high risk;
the first length threshold can be set according to the current network traffic protocol, and the risk is high when the data packet is too long or too short.
In the above embodiment, preferably, the determination rule further includes: and presetting an expression of an abnormal port number, wherein if the relationship between the port number of the source address of the data packet and the port number of the destination address of the data packet conforms to the expression of the abnormal port number, the network flow data packet is in a high risk level, and otherwise, the network flow data packet is in a low risk level.
Preferably, the system further includes a fourth preset database, where the fourth preset database stores a preset expression of an abnormal port number, and the expression indicates that, for example, when the network protocol of the packet is TCP, the port number of the source address of the packet is 30, and the port number of the destination address of the packet is 49, that the packet is an abnormal TCP port access, and the traffic packet is at a high risk level.
In any of the above embodiments, preferably, the system further includes an assignment module, where the assignment module is configured to assign a value according to the risk level of the network traffic packet, the source IP address of the data packet, the port number of the source address of the data packet, the port number of the destination address of the data packet, and the importance degree of the length of the data packet.
Characteristics such as a data packet source IP address, a port number of a data packet source address, a port number of a data packet destination address, a data packet length and the like can be graded and judged by an expert grading method, the importance degree of the characteristics is determined, and the assignment condition of the risk grade is shown in table 1:
TABLE 1
| Risk rating | High risk | Low risk |
| Assignment of value | 3 | 1 |
The relationship between the importance level and the importance coefficient is specifically shown in table 2:
TABLE 2
| Coefficient of importance | Of importance | Of moderate importance | Of general importance |
| Assignment of value | 2 | 1.5 | 1 |
In any of the above embodiments, preferably, the neural network unit includes an input portion, a neural network portion, and an integrated output portion, where the input portion, the neural network portion, and the integrated output portion are sequentially connected by signals, and an input factor of the input portion is a risk level of the network traffic packet after being assigned, and an importance degree of a source IP address of the data packet, a port number of a source address of the data packet, a port number of a destination address of the data packet, and a length of the data packet;
the neural network part is a BP neural network model with different hidden layer neuron numbers, and the range of the hidden layer neuron numbers is [6,10 ].
And the integrated output part integrates the output results of the BP neural network models by adopting a weighted average method, and the output result of the integrated output part is the final network traffic packet abnormal condition risk index value.
In any of the foregoing embodiments, preferably, the system further includes a marking unit, configured to mark an abnormal data packet when the network traffic data packet is determined to be the abnormal data packet, and store the corresponding abnormal data packet in a third preset database.
Referring to fig. 3, a second embodiment of the present invention provides a cloud platform traffic safety analysis method, including the following steps:
s1, collecting network flow data packets in real time and storing the network flow data packets in a first preset database;
s2, extracting the traffic characteristics of the network traffic packet stored in the first preset database, and storing the traffic characteristics in the second preset database;
s3, judging the risk level of the traffic packet by adopting a plurality of judgment rules based on the traffic characteristics;
s4, assigning the risk level of the network traffic packet and the importance degree of the traffic characteristics;
s5, establishing a neural network part comprising a plurality of BP neural network models for learning and training, and integrating training results of the plurality of BP neural network models to obtain a network traffic packet abnormal condition risk index value based on neural network integration;
and S6, marking the network traffic packet with higher network traffic packet abnormal situation risk index value as an abnormal traffic packet.
The cloud platform traffic safety analysis method disclosed by the second embodiment of the invention is a specific processing step of a cloud platform traffic safety analysis system, and the beneficial effects of the method are completely consistent with those of the first embodiment, and detailed description is not provided herein.
As can be seen from the above description, the embodiments of the present invention have the following beneficial effects: the BP neural network has strong nonlinear mapping capability and can be used for fitting complex functions.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A cloud platform traffic security analysis system, comprising:
the flow acquisition unit is used for acquiring a network flow data packet in real time and storing the network flow data packet into a first preset database;
the flow analysis unit is used for extracting the flow characteristics of the network flow packets stored in the first preset database and storing the flow characteristics into the second preset database;
and the neural network unit analyzes and judges the traffic characteristics stored in the second preset database based on the plurality of BP neural networks to determine whether the network traffic data packet is an abnormal data packet.
2. The cloud platform traffic safety analysis system according to claim 1, further comprising a marking unit, configured to mark an abnormal data packet and store the corresponding abnormal data packet in a third preset database when the network traffic data packet is determined to be the abnormal data packet.
3. The cloud platform traffic security analysis system of claim 1, wherein the traffic characteristics comprise: the packet source IP address, the port number of the packet source address, the port number of the packet destination address, and the packet length.
4. The cloud platform traffic safety analysis system according to claim 3, further comprising a judgment module, wherein a plurality of judgment rules for judging the risk level of the traffic packet are provided in the judgment module, and the judgment rules include:
if the occurrence frequency of the IP address of the data packet source is within the range of the first frequency threshold, the network flow data packet is at high risk;
if the occurrence frequency of the IP address of the data packet source is within the range of the second frequency threshold, the network flow data packet is in danger;
if the occurrence frequency of the source IP address of the data packet is within the range of the third frequency threshold, the network flow data packet is low in risk;
and if the length of the data packet is not greater than the first length threshold value, the network flow data packet is at low risk, otherwise, the network flow data packet is at high risk.
5. The cloud platform traffic security analysis system of claim 4, wherein the decision rule further comprises: and presetting an expression of an abnormal port number, wherein if the relationship between the port number of the source address of the data packet and the port number of the destination address of the data packet conforms to the expression of the abnormal port number, the network flow data packet is at a high risk, otherwise, the network flow data packet is at a low risk.
6. The cloud platform traffic safety analysis system according to claim 5, further comprising a fourth preset database, wherein an expression of a preset abnormal port number is stored in the fourth preset database.
7. The cloud platform traffic security analysis system of claim 6, further comprising an assignment module, wherein the assignment module is configured to assign a value according to a risk level of the network traffic packet and importance levels of a source IP address of the data packet, a port number of a source address of the data packet, a port number of a destination address of the data packet, and a length of the data packet.
8. The cloud platform traffic safety analysis system according to claim 7, wherein the neural network unit includes an input portion, a neural network portion, and an integrated output portion, the input portion, the neural network portion, and the integrated output portion are sequentially connected by signals, and input factors of the input portion are a risk level of the network traffic packet after being assigned, and importance degrees of a source IP address of the data packet, a port number of a source address of the data packet, a port number of a destination address of the data packet, and a length of the data packet;
the neural network part is a BP neural network model with different hidden layer neuron numbers, and the range of the hidden layer neuron numbers is [6,10 ].
9. The cloud platform traffic safety analysis system according to claim 8, wherein the integration output part integrates output results of the plurality of BP neural network models by using a weighted average method, and the output result of the integration output part is a final network traffic packet abnormal situation risk index value.
10. A cloud platform flow security analysis method is characterized by comprising the following steps:
s1, collecting network flow data packets in real time and storing the network flow data packets in a first preset database;
s2, extracting the traffic characteristics of the network traffic packet stored in the first preset database, and storing the traffic characteristics in the second preset database;
s3, judging the risk level of the traffic packet by adopting a plurality of judgment rules based on the traffic characteristics;
s4, assigning the risk level of the network traffic packet and the importance degree of the traffic characteristics;
s5, establishing a neural network part comprising a plurality of BP neural network models for learning and training, and integrating training results of the plurality of BP neural network models to obtain a network traffic packet abnormal condition risk index value based on neural network integration;
and S6, marking the network traffic packet with higher network traffic packet abnormal situation risk index value as an abnormal traffic packet.
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Application publication date: 20210312 |