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CN112637021A - Dynamic flow monitoring method and device based on linear regression algorithm - Google Patents

Dynamic flow monitoring method and device based on linear regression algorithm Download PDF

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Publication number
CN112637021A
CN112637021A CN202011622978.3A CN202011622978A CN112637021A CN 112637021 A CN112637021 A CN 112637021A CN 202011622978 A CN202011622978 A CN 202011622978A CN 112637021 A CN112637021 A CN 112637021A
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flow
data
linear regression
traffic
monitoring
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杨涛
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China Construction Bank Corp
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0876—Network utilisation, e.g. volume of load or congestion level
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/16—Threshold monitoring

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Environmental & Geological Engineering (AREA)
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Abstract

The application discloses a dynamic flow monitoring method and a device based on linear regression, which particularly collect flow data of each network node in a monitored network system; storing the flow data acquired all the time to form a flow database; monitoring the flow data by using a confidence interval obtained by processing the historical data based on a linear regression algorithm; and outputting abnormal flow information when the flow data exceeds the confidence interval. According to the scheme, the traffic is not monitored by adopting a fixed alarm threshold value, but the traffic data is monitored by utilizing the confidence intervals aiming at the change of the network traffic in different time periods, so that the monitoring accuracy is greatly improved.

Description

Dynamic flow monitoring method and device based on linear regression algorithm
Technical Field
The present application relates to the field of network technologies, and in particular, to a dynamic traffic monitoring method and apparatus based on a linear regression algorithm.
Background
When the current flow of the monitored network system exceeds the alarm threshold, an alarm is triggered. For most network systems, network traffic at different time periods also changes, and a fixed alarm threshold value is difficult to realize accurate monitoring of services, so that the monitoring accuracy is greatly reduced.
Disclosure of Invention
In view of this, the present application provides a dynamic traffic monitoring method and apparatus based on a linear regression algorithm, which are used to monitor traffic of a network system, so as to improve monitoring accuracy.
In order to achieve the above object, the following solutions are proposed:
a dynamic flow monitoring method based on a linear regression algorithm comprises the following steps:
collecting flow data of each network node in a monitored network system;
storing the flow data acquired all the time to form a flow database;
monitoring the flow data by using a confidence interval obtained by processing historical data based on a linear regression algorithm;
and outputting abnormal flow information when the flow data exceeds the confidence interval.
Optionally, the method further comprises the steps of:
and updating abnormal flow information in the flow database.
Optionally, the traffic data includes traffic size, acquisition time, acquisition node, traffic direction, and quintuple information.
Optionally, the method further comprises the steps of:
determining a monitored object according to the user requirement, and setting monitoring parameters of the monitored object;
acquiring historical flow data of the monitored object based on the monitoring parameters;
processing the historical flow data by using a linear regression algorithm to obtain a linear regression function of the flow data to time;
and calculating the confidence interval according to normal distribution based on the linear regression function.
Optionally, the confidence interval includes a current flow prediction value and an alarm threshold.
A dynamic flow monitoring device based on a linear regression algorithm, the dynamic flow monitoring device comprising:
the data acquisition module is used for acquiring the flow data of each network node in the monitored network system;
the data storage module is used for storing the flow data acquired in the past to form a flow database;
the data detection module is used for monitoring the flow data by using a confidence interval obtained by processing historical data based on a linear regression algorithm;
and the result output module is used for outputting abnormal flow information when the flow data exceeds the confidence interval.
Optionally, the method further includes:
and the data updating module is used for updating the abnormal flow information in the flow database.
Optionally, the traffic data includes traffic size, acquisition time, acquisition node, traffic direction, and quintuple information.
Optionally, the method further includes:
the parameter setting module is used for determining a monitored object according to the user requirement and setting the monitoring parameters of the monitored object;
the data acquisition module is used for acquiring historical flow data of the monitored object based on the monitoring parameters;
the function construction module is used for processing the historical flow data by using a linear regression algorithm to obtain a linear regression function of the flow data to time;
and the interval calculation module is used for calculating the confidence interval based on the linear regression function and according to normal distribution.
Optionally, the confidence interval includes a current flow prediction value and an alarm threshold.
From the technical scheme, the application discloses a dynamic flow monitoring method and a device based on linear regression, and particularly collects flow data of each network node in a monitored network system; storing the flow data acquired all the time to form a flow database; monitoring the flow data by using a confidence interval obtained by processing the historical data based on a linear regression algorithm; and outputting abnormal flow information when the flow data exceeds the confidence interval. According to the scheme, the traffic is not monitored by adopting a fixed alarm threshold value, but the traffic data is monitored by utilizing the confidence intervals aiming at the change of the network traffic in different time periods, so that the monitoring accuracy is greatly improved.
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In order to more clearly illustrate the embodiments of the present application 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 application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a dynamic flow monitoring method based on a linear regression algorithm according to an embodiment of the present application;
FIG. 2 is a flowchart of another dynamic flow monitoring method based on a linear regression algorithm according to an embodiment of the present application;
FIG. 3 is a flow chart of a method of calculating a confidence interval in an embodiment of the present application;
fig. 4 is a block diagram of a dynamic flow monitoring apparatus based on a linear regression algorithm according to an embodiment of the present application;
FIG. 5 is a block diagram of another dynamic flow monitoring device based on a linear regression algorithm according to an embodiment of the present application;
fig. 6 is a block diagram of another dynamic flow monitoring device based on a linear regression algorithm according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Example one
Fig. 1 is a flowchart of a dynamic flow monitoring method based on a linear regression algorithm according to an embodiment of the present application.
As shown in fig. 1, the dynamic traffic monitoring method provided in this embodiment is not based on a fixed alarm threshold to monitor real-time traffic, but based on a dynamic confidence interval, and includes the following steps:
s101, collecting flow data of each network node.
The method comprises the steps of collecting flow data of each network node in real time aiming at each network node in a monitored network system, wherein the collection time interval is o. The collected flow data comprises flow size, collection time, collection nodes, flow direction and quintuple information.
And S102, storing the flow data acquired all the time.
Namely, the flow data collected at all times are stored and managed, so that a flow database is formed.
And S103, monitoring the flow data by using the confidence interval.
The confidence interval is a numerical range obtained by processing historical data based on a linear regression algorithm, and the confidence interval not only comprises the current flow predicted value, but also comprises an alarm threshold value, wherein the alarm threshold value belongs to the confidence interval and is not a fixed numerical value.
And S104, outputting abnormal flow information when the flow data exceeds the confidence interval.
By monitoring the flow data, when the flow data exceeds the alarm threshold, abnormal flow information is output to the user to prompt the user or operation and maintenance personnel that the flow exceeds the limit.
As can be seen from the above technical solutions, the present embodiment provides a dynamic traffic monitoring method based on linear regression, specifically to collect traffic data of each network node in a monitored network system; storing the flow data acquired all the time to form a flow database; monitoring the flow data by using a confidence interval obtained by processing the historical data based on a linear regression algorithm; and outputting abnormal flow information when the flow data exceeds the confidence interval. According to the scheme, the traffic is not monitored by adopting a fixed alarm threshold value, but the traffic data is monitored by utilizing the confidence intervals aiming at the change of the network traffic in different time periods, so that the monitoring accuracy is greatly improved.
In one embodiment of the present application, the following steps are also included, as shown in fig. 2.
And S105, updating abnormal flow data in the flow database by using the current flow predicted value.
When the user or operation and maintenance personnel obtains the abnormal flow information output by the scheme, the current alarm flow is confirmed, and if the abnormal flow information is determined, the flow data is updated to the flow database, so that the subsequent calculation of the confidence interval is more accurate.
The application also includes a step for obtaining the confidence interval by a linear regression algorithm, as shown in fig. 3.
S201, determining a monitored object and monitoring parameters thereof.
Namely, the monitored object to be monitored is determined according to the requirement of the user, and the monitoring parameter of the monitored object is set. The monitoring parameters include historical data time unit T (such as hour, day, week, month, quarter, year, etc.), collected data number N, recent historical data reference time length M (unit is o), historical data weight parameter gamma, etc.
S202, collecting historical flow data of the monitored object based on the monitoring parameters.
And acquiring the previous N data with time interval T of the monitored object, namely [ R1, R2,. and RN ], and the current latest M monitoring data P [ P1, P2,. and PM ] from the flow database according to the monitoring parameters.
And S203, obtaining a linear regression function based on a linear regression algorithm.
A linear regression algorithm is used to calculate a linear function of the flow data Q over time t:
QR(t)＝αRt+βR+τR
QP(t)＝αPt+βP+τP
wherein QR(t) and QPCalculated for R and P data, respectivelyTo a linear regression function, alphaR、αPAnd betaR、βPThe parameters of the two linear regression functions are respectively expressed as follows:
Figure BDA0002878687620000051
Figure BDA0002878687620000052
Figure BDA0002878687620000061
Figure BDA0002878687620000062
wherein T isRiAnd QRi、TPiAnd QPiEach is R ═ R1, R2]And P ═ P1, P2]The acquisition time and the acquisition flow rate in the data,
Figure BDA0002878687620000063
and
Figure BDA0002878687620000064
the average of the flow and time in the R and P datasets, respectively.
τRAnd τPIs a random variable, and is an error term of linear regression function calculated for R and P data respectively, and obeys normal distribution
Figure BDA0002878687620000065
And S204, obtaining a confidence interval based on the linear regression function.
Based on the linear regression function and according to the normal distribution function, tau can be obtainedRAnd τPThe confidence intervals of (a) are respectively:
Figure BDA0002878687620000066
Figure BDA0002878687620000067
wherein
Figure BDA0002878687620000068
Indicates the statistical value, σ, at the confidence level (1- θ)RAnd σPAre each tauRAnd τPStandard deviation calculated in linear regression.
According to the above calculation results, the predicted value Q (t) and the alarm threshold of the current flow of the monitored object can be calculated
Figure BDA0002878687620000069
Respectively as follows:
Q(t)＝γQR(t)+(1-γ)QP(t)
Figure BDA00028786876200000610
where gamma is the historical traffic weight parameter.
Example two
Fig. 4 is a block diagram of a dynamic flow monitoring apparatus based on a linear regression algorithm according to an embodiment of the present application.
As shown in fig. 4, the dynamic flow monitoring apparatus provided in this embodiment is not based on a fixed alarm threshold to monitor real-time flow, but based on a dynamic confidence interval, and includes a data acquisition module 10, a data storage module 20, a data detection module 30, and a result output module 40.
The data acquisition module is used for acquiring the flow data of each network node.
The method comprises the steps of collecting flow data of each network node in real time aiming at each network node in a monitored network system, wherein the collection time interval is o. The collected flow data comprises flow size, collection time, collection nodes, flow direction and quintuple information.
The data storage module is used for storing the flow data acquired all the time.
The acquired flow data at all times are stored and managed in a short range, and therefore a flow database is formed.
The data detection module is used for monitoring the flow data by utilizing the confidence interval.
The confidence interval is a numerical range obtained by processing historical data based on a linear regression algorithm, and the confidence interval not only comprises the current flow predicted value, but also comprises an alarm threshold value, wherein the alarm threshold value belongs to the confidence interval and is not a fixed numerical value.
And the result output module is used for outputting abnormal flow information when the flow data exceeds the confidence interval.
By monitoring the flow data, when the flow data exceeds the alarm threshold, abnormal flow information is output to the user to prompt the user or operation and maintenance personnel that the flow exceeds the limit.
It can be seen from the above technical solutions that the present embodiment provides a dynamic traffic monitoring apparatus based on linear regression, specifically, collects traffic data of each network node in a monitored network system; storing the flow data acquired all the time to form a flow database; monitoring the flow data by using a confidence interval obtained by processing the historical data based on a linear regression algorithm; and outputting abnormal flow information when the flow data exceeds the confidence interval. According to the scheme, the traffic is not monitored by adopting a fixed alarm threshold value, but the traffic data is monitored by utilizing the confidence intervals aiming at the change of the network traffic in different time periods, so that the monitoring accuracy is greatly improved.
In one embodiment of the present application, a data update module 50 is further included, as shown in fig. 5.
And the data updating module is used for updating the current flow predicted value to the flow database.
When the user or operation and maintenance personnel obtains the abnormal flow information output by the scheme, the current alarm flow is confirmed, and if the abnormal flow information is determined, the flow data is updated to the flow database, so that the subsequent calculation of the confidence interval is more accurate.
In another specific implementation manner of this embodiment, the apparatus further includes a parameter setting module 60, a data obtaining module 70, a function constructing module 80, and an interval calculating module 90, configured to obtain the confidence interval through a linear regression algorithm, as shown in fig. 6.
The parameter setting module is used for determining the monitored object and the monitored parameters thereof.
Namely, the monitored object to be monitored is determined according to the requirement of the user, and the monitoring parameter of the monitored object is set. The monitoring parameters include historical data time unit T (such as hour, day, week, month, quarter, year, etc.), collected data number N, recent historical data reference time length M (unit is o), historical data weight parameter gamma, etc.
The data acquisition module is used for acquiring historical flow data of the monitored object based on the monitoring parameters.
And acquiring the previous N data with time interval T of the monitored object, namely [ R1, R2,. and RN ], and the current latest M monitoring data P [ P1, P2,. and PM ] from the flow database according to the monitoring parameters.
The function building module obtains a linear regression function based on a linear regression algorithm.
A linear regression algorithm pair was used to calculate a linear function of the flow data Q versus time t:
QR(t)＝αRt+βR+τR
QP(t)＝αPt+βP+τP
wherein QR(t) and QP(t) is a linear regression function calculated from R and P data, respectively, alphaR、αPAnd betaR、βPThe parameters of the two linear regression functions are respectively expressed as follows:
Figure BDA0002878687620000081
Figure BDA0002878687620000082
Figure BDA0002878687620000083
Figure BDA0002878687620000084
wherein T isRiAnd QRi、TPiAnd QPiEach is R ═ R1, R2]And P ═ P1, P2]The acquisition time and the acquisition flow rate in the data,
Figure BDA0002878687620000091
and
Figure BDA0002878687620000092
the average of the flow and time in the R and P datasets, respectively.
τRAnd τPIs a random variable, and is an error term of linear regression function calculated for R and P data respectively, and obeys normal distribution
Figure BDA0002878687620000093
The interval calculation module is used for obtaining a confidence interval based on a linear regression function.
Based on the linear regression function and according to the normal distribution function, tau can be obtainedRAnd τPThe confidence intervals of (a) are respectively:
Figure BDA0002878687620000094
Figure BDA0002878687620000095
wherein
Figure BDA0002878687620000096
Statistical value, σ, at a good confidence level (1- θ)RAnd σPAre each tauRAnd τPStandard deviation calculated in linear regression.
According to the above calculation results, the predicted value Q (t) and the alarm threshold of the current flow of the monitored object can be calculated
Figure BDA0002878687620000097
Respectively as follows:
Q(t)＝γQR(t)+(1-γ)QP(t)
Figure BDA0002878687620000098
where gamma is the historical traffic weight parameter.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The technical solutions provided by the present invention are described in detail above, and the principle and the implementation of the present invention are explained in this document by applying specific examples, and the descriptions of the above examples are only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. A dynamic flow monitoring method based on a linear regression algorithm is characterized by comprising the following steps:
collecting flow data of each network node in a monitored network system;
storing the flow data acquired all the time to form a flow database;
monitoring the flow data by using a confidence interval obtained by processing historical data based on a linear regression algorithm;
and outputting abnormal flow information when the flow data exceeds the confidence interval.
2. The dynamic traffic monitoring method according to claim 1, further comprising the steps of:
and storing the abnormal flow information into the flow database.
3. The dynamic traffic monitoring method according to claim 1, wherein the traffic data includes traffic size, acquisition time, acquisition node, traffic direction, and quintuple information.
4. The dynamic traffic monitoring method according to claim 1, further comprising the steps of:
determining a monitored object according to the user requirement, and setting monitoring parameters of the monitored object;
acquiring historical flow data of the monitored object based on the monitoring parameters;
processing the historical flow data by using a linear regression algorithm to obtain a linear regression function of the flow data to time;
and calculating a confidence interval of the flow by utilizing normal distribution based on the linear regression function.
5. The dynamic traffic monitoring method of claim 4, wherein the confidence interval includes a current traffic prediction value and an alarm threshold.
6. A dynamic flow monitoring device based on a linear regression algorithm, the dynamic flow monitoring device comprising:
the data acquisition module is used for acquiring the flow data of each network node in the monitored network system;
the data storage module is used for storing the flow data acquired in the past to form a flow database;
the data detection module is used for monitoring the flow data by using a confidence interval obtained by processing historical data based on a linear regression algorithm;
and the result output module is used for outputting abnormal flow information when the flow data exceeds the confidence interval.
7. The dynamic flow monitoring device of claim 6, further comprising:
and the data updating module is used for updating the abnormal flow information in the flow database.
8. The dynamic traffic monitoring device of claim 6, wherein the traffic data includes traffic size, acquisition time, acquisition node, traffic direction, and quintuple information.
9. The dynamic flow monitoring device of claim 6, further comprising:
the parameter setting module is used for determining a monitored object according to the user requirement and setting the monitoring parameters of the monitored object;
the data acquisition module is used for acquiring historical flow data of the monitored object based on the monitoring parameters;
the function construction module is used for processing the historical flow data by using a linear regression algorithm to obtain a linear regression function of the flow data to time;
and the interval calculation module is used for calculating the confidence interval based on the linear regression function and according to normal distribution.
10. The dynamic flow monitoring device of claim 9, wherein the confidence interval includes a current flow prediction value and an alarm threshold.
CN202011622978.3A 2020-12-31 2020-12-31 Dynamic flow monitoring method and device based on linear regression algorithm Pending CN112637021A (en)

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CN114285612A (en) * 2021-12-14 2022-04-05 北京天融信网络安全技术有限公司 Method, system, device, equipment and medium for detecting abnormal data
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Application publication date: 20210409