Disclosure of Invention
The invention aims to provide a method and a system for tracing the security vulnerability of streaming data in a video conference scene so as to improve the data security performance in the video conference scene.
In order to achieve the purpose, the invention provides the following scheme:
a tracing method for security vulnerabilities of streaming data under a video conference scene comprises the following steps:
acquiring conference stream data generated by video conference transmission information; the conference flow data comprises flow data of each function module combination to be tested;
cutting the stream data of each function module combination to be tested into a plurality of stream data fragments to obtain a stream data fragment sequence corresponding to each function module to be tested;
according to the stream data characteristics, extracting characteristics of each stream data fragment in the stream data fragment sequence corresponding to each function module to be tested to obtain a characteristic matrix corresponding to each function module to be tested; a p line of the characteristic matrix is a characteristic vector corresponding to a p stream data segment, and a q column represents a characteristic value of a q stream data characteristic corresponding to each stream data segment;
for the t functional module to be tested, data grouping is carried out according to the characteristic matrix corresponding to the t functional module to be tested to obtainkA set of features; each characteristic group comprises a plurality of columns of data in a characteristic matrix corresponding to the tth functional module to be tested;
acquiring detection times; number of detections
rTo satisfy
The smallest integer value of (c);
determining a feature group set of each detection; the index j of the jth characteristic group is represented by a binary number group, and the characteristic group set detected at the ith time is a set formed by all characteristic groups corresponding to the indexes with the ith bit of the binary number group being 1;
grading the feature group set of each detection by using a grading model to obtain a grading vector of each detection; the scoring vector is a k-dimensional vector, and when the ith detected feature group set does not comprise a jth feature group, the value of the jth position in the ith detected scoring vector is null; when the feature group set of the ith detection comprises the jth feature group, the value of the jth position in the scoring vector of the ith detection is a scoring value;
when the detection times are reached, obtaining a scoring matrix of the tth functional module to be tested; the ith row of the scoring matrix is a scoring vector of the ith detection, and the jth column is a scoring score of each detection of the jth feature group;
determining a scoring result of each feature group according to the scoring matrix of the tth functional module to be tested; the scoring result of each feature group is the scoring average value of all the detection times of the feature group;
determining the stream data characteristics corresponding to the security vulnerability of the tth functional module to be tested according to the grading result of each characteristic group and the stream data characteristics corresponding to each characteristic group;
and sequentially determining the stream data characteristics corresponding to the security vulnerabilities of each functional module to be tested.
Optionally, the acquiring conference stream data generated by the video conference transmission information specifically includes:
determining a functional module to be tested in the video conference;
generating a conference flow data acquisition scheme; the conference flow data acquisition scheme is a scheme for sequentially acquiring data of the functional modules to be tested;
according to the conference flow data acquisition scheme, monitoring the video conference in progress by using a network sniffing tool, storing the conference flow data of each video monitoring and generating a label of each video monitoring; and the conference stream data monitored by the video at each time is the stream data of the currently monitored functional module to be tested.
Optionally, the stream data characteristics include: number of bits per second, number of uploaded bits per second, number of downloaded bits per second, proportion of the number of uploaded or downloaded bits, number of packets per second, number of uploaded packets per second, number of downloaded packets per second, proportion of the number of uploaded or downloaded packets, mean/variance of packet size and 25% segmentation point, packet length distribution frequency sequence, packet length transfer matrix, mean/variance of uploaded packet size and 25% segmentation point, mean/variance of downloaded packet size and 25% segmentation point, upload packet length distribution frequency sequence, download packet length distribution frequency sequence, upload packet length transfer matrix, download packet length transfer matrix, packet time interval mean/variance and 25% segmentation point, packet time interval distribution, upload packet time interval mean/variance and 25% segmentation point, download packet time interval mean/variance and 25% segmentation point, Upload packet time interval, download packet time interval, variance/kurtosis and skewness of the traffic sequence, variance/kurtosis and skewness of the upload traffic sequence, and variance/kurtosis and skewness of the download traffic sequence.
Optionally, the scoring the feature group set detected each time by using the scoring model to obtain a scoring vector detected each time includes:
using formulas
Scoring each feature group in the feature group set of the current detection to obtain a scoring vector of the current detection
,
(ii) a Wherein,
for the first in the set of currently detected feature groups
iA score for the individual feature set;
representing the recall ratio of the classification model;
representing the precision of the classification model.
Optionally, the determining, according to the scoring result of each feature group, a stream data feature corresponding to the security vulnerability of the tth functional module to be tested by combining the stream data feature corresponding to each feature group specifically includes:
screening T feature groups with the highest scoring results;
determining a plurality of stream data characteristics corresponding to each characteristic group in the T characteristic groups to obtain a stream data characteristic set;
and determining the stream data characteristics with the largest occurrence frequency in the stream data characteristic set as the stream data characteristics corresponding to the security vulnerability of the tth function module to be tested.
The invention also provides a system for tracing the security vulnerabilities of streaming data in a video conference scene, which comprises the following steps:
the conference flow data acquisition module is used for acquiring conference flow data generated by video conference transmission information; the conference flow data comprises flow data of each function module combination to be tested;
the flow data cutting module is used for cutting the flow data of each to-be-tested function module combination into a plurality of flow data fragments to obtain a flow data fragment sequence corresponding to each to-be-tested function module;
the characteristic extraction module is used for extracting the characteristics of each flow data segment in the flow data segment sequence corresponding to each function module to be tested according to the flow data characteristics to obtain a characteristic matrix corresponding to each function module to be tested; a p line of the characteristic matrix is a characteristic vector corresponding to a p stream data segment, and a q column represents a characteristic value of a q stream data characteristic corresponding to each stream data segment;
a characteristic grouping module for grouping data of the tth functional module to be tested according to the characteristic matrix corresponding to the tth functional module to be tested to obtainkA set of features; each characteristic group comprises a plurality of columns of data in a characteristic matrix corresponding to the tth functional module to be tested;
the detection times acquisition module is used for acquiring detection times; number of detections
rTo satisfy
The smallest integer value of (c);
the characteristic group set determining module is used for determining a characteristic group set detected each time; the index j of the jth characteristic group is represented by a binary number group, and the characteristic group set detected at the ith time is a set formed by all characteristic groups corresponding to the indexes with the ith bit of the binary number group being 1;
the scoring module is used for scoring the feature group set of each detection by using a scoring model to obtain a scoring vector of each detection; the scoring vector is a k-dimensional vector, and when the ith detected feature group set does not comprise a jth feature group, the value of the jth position in the ith detected scoring vector is null; when the feature group set of the ith detection comprises the jth feature group, the value of the jth position in the scoring vector of the ith detection is a scoring value;
the scoring matrix determining module is used for obtaining a scoring matrix of the tth to-be-tested functional module after the detection times are reached; the ith row of the scoring matrix is a scoring vector of the ith detection, and the jth column is a scoring score of each detection of the jth feature group;
the scoring result determining module is used for determining the scoring result of each feature group according to the scoring matrix of the tth to-be-tested function module; the scoring result of each feature group is the scoring average value of all the detection times of the feature group;
the security vulnerability determining module is used for determining the stream data characteristics corresponding to the security vulnerability of the tth functional module to be tested according to the grading result of each characteristic group and by combining the stream data characteristics corresponding to each characteristic group; and determining the stream data characteristics corresponding to the security vulnerabilities of each functional module to be tested in sequence.
Optionally, the conference stream data acquiring module specifically includes:
the to-be-tested function module determining unit is used for determining the to-be-tested function module in the video conference;
the conference flow data acquisition scheme generating unit is used for generating a conference flow data acquisition scheme; the conference flow data acquisition scheme is a scheme for sequentially acquiring data of the functional modules to be tested;
the data acquisition unit is used for monitoring the video conference in progress by using a network sniffing tool according to the conference stream data acquisition scheme, storing the conference stream data of each video monitoring and generating a label of each video monitoring; and the conference stream data monitored by the video at each time is the stream data of the currently monitored functional module to be tested.
Optionally, the stream data characteristics include: number of bits per second, number of uploaded bits per second, number of downloaded bits per second, proportion of the number of uploaded or downloaded bits, number of packets per second, number of uploaded packets per second, number of downloaded packets per second, proportion of the number of uploaded or downloaded packets, mean/variance of packet size and 25% segmentation point, packet length distribution frequency sequence, packet length transfer matrix, mean/variance of uploaded packet size and 25% segmentation point, mean/variance of downloaded packet size and 25% segmentation point, upload packet length distribution frequency sequence, download packet length distribution frequency sequence, upload packet length transfer matrix, download packet length transfer matrix, packet time interval mean/variance and 25% segmentation point, packet time interval distribution, upload packet time interval mean/variance and 25% segmentation point, download packet time interval mean/variance and 25% segmentation point, Upload packet time interval, download packet time interval, variance/kurtosis and skewness of the traffic sequence, variance/kurtosis and skewness of the upload traffic sequence, and variance/kurtosis and skewness of the download traffic sequence.
Optionally, the scoring module specifically includes:
a scoring unit for utilizing a formula
Scoring each feature group in the feature group set of the current detection to obtain a scoring vector of the current detection
,
(ii) a Wherein,
for the first in the set of currently detected feature groups
iA score for the individual feature set;
representing the recall ratio of the classification model;
representing the precision of the classification model.
Optionally, the security vulnerability determining module specifically includes:
the characteristic group screening unit is used for screening T characteristic groups with the highest scoring results;
the flow data characteristic determining unit is used for determining a plurality of flow data characteristics corresponding to each characteristic group in the T characteristic groups to obtain a flow data characteristic set;
and the security vulnerability determining unit is used for determining the stream data feature with the largest occurrence frequency in the stream data feature set as the stream data feature corresponding to the security vulnerability of the tth function module to be tested.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the invention, by grouping, detecting, positioning and tracing suspicious features, the source features exposing data privacy in streaming data are detected quickly, accurately and efficiently, so that a transmission security vulnerability warning is provided for a protector, and the data security performance of a video conference is improved; through the characteristic grouping scheme, each group has unique characteristic detection points, and meanwhile, the detection times are reduced, so that the detection is efficient and accurate.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic flow chart of a method for tracing security vulnerabilities of streaming data in a video conference scene according to the present invention. As shown in fig. 1, the method for tracing the security vulnerabilities of streaming data in a video conference scene of the present invention includes the following steps:
step 100: and acquiring conference stream data generated by the video conference transmission information. And the conference flow data comprises the flow data of each function module combination to be tested. The method generates a conference flow data acquisition scheme according to the functional modules to be tested, and then acquires the flow data of each functional module combination to be tested according to the conference flow data acquisition scheme, thereby obtaining the conference flow data. The specific process is as follows:
step 1: and analyzing the functional modules to be tested in the video conference, namely the functional modules to be tested. For functional modules to be tested
It is shown that,
in total comprise
And the function module to be tested depends on the streaming data transmission function.
Step 2: and generating a conference flow data acquisition scheme. The method comprises the following steps:
step 2-1: structure of the device
Binary digit of bit
An initial value of
. If it is
To (1) a
The bit is 1, which represents the functional module used by the current round of conference
The collected flow data comprises a function module
(ii) a If it is
To (1) a
Bit is 0, indicating a functional module
Unused and collected flow data does not contain functional modules
。
Step 2-2: make it
Gradually increase until the value is
。
Step 2-3: record all scenarios
Namely, the conference flow data acquisition scheme is obtained.
Step 3: and acquiring video conference stream data according to a conference stream data acquisition scheme. Using flow rateThe analysis tool (such as WireShark, Scapy, etc.) listens to the video conference in progress, saves conference stream data of each video listening, and will send the conference stream data to the analysis tool
As a tag for each video listen.
Step 4: and (5) preprocessing stream data. Reading a physical address of a current test device
The physical address of the source end and the physical address of the physical end in the obtained conference stream data are not
The data of (1).
Step 200: and cutting the stream data of each function module combination to be tested into a plurality of stream data fragments to obtain a stream data fragment sequence corresponding to each function module to be tested. Determining the number of segments in each meeting according to the time length of each meeting
mTo the label is
Per each fragment of the stream data
And cutting every second to obtain a stream data fragment sequence corresponding to each functional module to be tested. Then, the number of the data pieces of each label is equal through the form of data piece alignment, and the obtained data pieces
The matrix of stream data fragments of (a),
nfor the number of functional modules to be tested, the
iIs listed as the first
iThe sequence of the stream data fragments corresponding to the functional module to be tested is expressed as
The corresponding label is
。
Step 300: and according to the stream data characteristics, performing characteristic extraction on each stream data fragment in the stream data fragment sequence corresponding to each function module to be tested to obtain a characteristic matrix corresponding to each function module to be tested. The p-th row of the feature matrix is a feature vector corresponding to the p-th stream data segment, and the q-th column represents a feature value of the q-th stream data feature corresponding to each stream data segment.
The stream data features of the present invention include: number of bits per second, number of uploaded bits per second, number of downloaded bits per second, proportion of the number of uploaded or downloaded bits, number of packets per second, number of uploaded packets per second, number of downloaded packets per second, proportion of the number of uploaded or downloaded packets, mean/variance of packet size and 25% segmentation point, packet length distribution frequency sequence, packet length transfer matrix, mean/variance of uploaded packet size and 25% segmentation point, mean/variance of downloaded packet size and 25% segmentation point, upload packet length distribution frequency sequence, download packet length distribution frequency sequence, upload packet length transfer matrix, download packet length transfer matrix, packet time interval mean/variance and 25% segmentation point, packet time interval distribution, upload packet time interval mean/variance and 25% segmentation point, download packet time interval mean/variance and 25% segmentation point, The upload packet time interval, the download packet time interval, the variance/kurtosis and skewness of the flow sequence, the variance/kurtosis and skewness of the upload flow sequence, the variance/kurtosis and skewness of the download flow sequence, and the like, and partial features can be selected according to actual situations to perform feature extraction.
The specific process for feature extraction is as follows:
step 1: and determining a characteristic value extraction scheme. All the stream data characteristics described above may be used, or some of the stream data characteristics may be selected according to the actual situation.
Step 2: reading
Each of the stream data fragments
Root of Chinese angelicaExtracting stream data segments according to a characteristic value extraction scheme
The characteristic value of each stream data characteristic forms a characteristic vector
。
Step 3: repeat Step2 until the total number is
Stream data fragment of
All converted into eigenvectors to form a matrix
I.e. is the first
iAnd the characteristic matrix corresponds to each functional module to be tested.
Step 4: repeating Step3 until n groups of data are all extracted, and forming a feature matrix of the whole conference data stream
Inheriting the corresponding label matrix from the conference generation scheme
。
Step 400: for the t functional module to be tested, data grouping is carried out according to the characteristic matrix corresponding to the t functional module to be tested to obtainkAnd (4) a feature group. Each characteristic group comprises a plurality of columns of data in a characteristic matrix corresponding to the tth functional module to be tested. The specific process is as follows:
step 1: and designing a grouping strategy. Grouping data according to grouping rules shown in Table 1, and selecting one of the data according to historical security vulnerabilities
Group as the tth function to be testedFinal of the module
kAnd (4) a feature group.
TABLE 1 characteristic grouping rules
Step 500: and acquiring detection times. If the data is divided intokGroup, then number of probing scenariosrTo satisfy 2 r ≥ k+1 takes the smallest integer value.
Step 600: a set of feature sets for each detection is determined. The index j of the jth characteristic group is represented by a binary number group, and the characteristic group set detected at the ith time is a set formed by characteristic groups corresponding to all indexes with the ith bit of 1 in the binary number group.
Step 700: and scoring the feature group set of each detection by using a scoring model to obtain a scoring vector of each detection. The scoring model is
,
For the first in the set of currently detected feature groups
iA score for the individual feature set;
representing the recall ratio of the classification model;
representing the precision of the classification model. The scoring vector is a k-dimensional vector, and when the ith detected feature group set does not comprise a jth feature group, the value of the jth position in the ith detected scoring vector is null; and when the feature group set of the ith detection comprises the jth feature group, the value of the jth position in the scoring vector of the ith detection is the scoring value.
Step 800: and when the detection times are reached, obtaining a scoring matrix of the t-th functional module to be tested. The ith row of the scoring matrix is the scoring vector of the ith detection, and the jth column is the scoring score of each detection of the jth feature group.
To be provided withkIf =5, the number of detections isrAnd = 3. At this timekThe individual feature set representations are shown in table 2.
TABLE 2 feature set representation
[0001]Number of feature sets
|
[0002]Characteristic group 1
|
[0003]Feature group 2
|
[0004]Feature group 3
|
[0005]Feature group 4
|
[0006]Feature group 5
|
[0007]Index
|
[0008] 001
|
[0009] 010
|
[0010] 011
|
[0011] 100
|
[0012] 101 |
Detecting for the first time: i =1, and the feature group with the 1 st bit of the small endian being 1 in the binary number group of the index, i.e., feature group 1, feature group 3, and feature group 5, is selected to obtain a score F1.
And (3) second detection: i =2, the feature group with the small endian 2 nd bit of 1 in the binary number group of the index, i.e., feature group 2 and feature group 3, is selected, and a score F2 is obtained.
And (3) third detection: i =3, the feature group with the small endian 3 rd bit of 1 in the binary number group of the index, i.e., feature group 4 and feature group 5, is selected to obtain a score F3.
Then obtain the scoring matrix of
。
Step 900: and determining the scoring result of each feature group according to the scoring matrix of the tth functional module to be tested. The scoring result of each feature group is the scoring average value of all the detections of the feature group, that is, each column of the scoring matrix is averaged to obtain the scoring average value of the feature group corresponding to the column.
Step 1000: and determining the stream data characteristics corresponding to the security vulnerability of the tth functional module to be tested according to the grading result of each characteristic group and the stream data characteristics corresponding to each characteristic group. Specifically, firstly, screening T feature groups with the highest scoring result, namely positioning key features, and screening the feature groups with the highest traceable privacy exposure, namely corresponding security holes; then, determining a plurality of stream data characteristics corresponding to each characteristic group in the T characteristic groups to obtain a stream data characteristic set; and finally, determining the stream data characteristics with the largest occurrence frequency in the stream data characteristic set as the stream data characteristics corresponding to the security vulnerability of the tth function module to be tested.
Step 1100: and sequentially determining the stream data characteristics corresponding to the security vulnerabilities of each functional module to be tested. The security vulnerability detection of each functional module to be tested in the video conference can be realized.
Based on the method for tracing the security vulnerabilities of the streaming data under the video conference scene, the invention also provides a method for tracing the security vulnerabilities of the streaming data under the video conference scene, and fig. 2 is a schematic structural diagram of the system for tracing the security vulnerabilities of the streaming data under the video conference scene. As shown in fig. 2, the system for tracing security vulnerabilities of streaming data in a video conference scene of the present invention includes:
a conference stream data obtaining module 201, configured to obtain conference stream data generated by video conference transmission information; and the conference flow data comprises flow data of each function module combination to be tested.
The stream data cutting module 202 is configured to cut the stream data of each combination of the function modules to be tested into a plurality of stream data segments, so as to obtain a stream data segment sequence corresponding to each function module to be tested.
The feature extraction module 203 is configured to perform feature extraction on each stream data segment in the stream data segment sequence corresponding to each function module to be tested according to the stream data features to obtain a feature matrix corresponding to each function module to be tested; the p-th row of the feature matrix is a feature vector corresponding to the p-th stream data segment, and the q-th column represents a feature value of the q-th stream data feature corresponding to each stream data segment.
A feature grouping module 204, configured to, for the tth functional module to be tested, perform data grouping according to the feature matrix corresponding to the tth functional module to be tested to obtain a feature matrixkA set of features; each characteristic group comprises multi-column data in a characteristic matrix corresponding to the tth functional module to be tested.
A detection
number obtaining module 205, configured to obtain a detection number; number of detections
rTo satisfy
The smallest integer value of (c).
A feature set determining module 206, configured to determine a feature set for each detection; the index j of the jth characteristic group is represented by a binary number group, and the characteristic group set detected at the ith time is a set formed by characteristic groups corresponding to all indexes with the ith bit of 1 in the binary number group.
The scoring module 207 is configured to score the feature group set of each detection by using a scoring model to obtain a scoring vector of each detection; the scoring vector is a k-dimensional vector, and when the ith detected feature group set does not comprise a jth feature group, the value of the jth position in the ith detected scoring vector is null; and when the feature group set of the ith detection comprises the jth feature group, the value of the jth position in the scoring vector of the ith detection is the scoring value.
A scoring matrix determining module 208, configured to obtain a scoring matrix of the tth to-be-tested function module after the detection times are reached; the ith row of the scoring matrix is a scoring vector of the ith detection, and the jth column is a scoring score of each detection of the jth feature group.
A scoring result determining module 209, configured to determine a scoring result of each feature group according to the scoring matrix of the tth to-be-tested function module; the scoring result for each feature set is the average of the scores for all of the probes of that feature set.
The security vulnerability determining module 2010 is configured to determine, according to the scoring result of each feature group, stream data features corresponding to the security vulnerability of the tth functional module to be tested in combination with the stream data features corresponding to each feature group; and determining the stream data characteristics corresponding to the security vulnerabilities of each functional module to be tested in sequence.
As a specific embodiment, in the system for tracing to the source of the security vulnerability of the streaming data in the video conference scene, the conference streaming data obtaining module 201 specifically includes:
and the to-be-tested function module determining unit is used for determining the to-be-tested function module in the video conference.
The conference flow data acquisition scheme generating unit is used for generating a conference flow data acquisition scheme; the conference flow data acquisition scheme is a scheme for sequentially acquiring the data of the functional modules to be tested.
The data acquisition unit is used for monitoring the video conference in progress by using a network sniffing tool according to the conference stream data acquisition scheme, storing the conference stream data of each video monitoring and generating a label of each video monitoring; and the conference stream data monitored by the video at each time is the stream data of the currently monitored functional module to be tested.
As a specific embodiment, in the system for tracing to the source of the security vulnerability of the streaming data in the video conference scene, the characteristics of the streaming data include: number of bits per second, number of uploaded bits per second, number of downloaded bits per second, proportion of the number of uploaded or downloaded bits, number of packets per second, number of uploaded packets per second, number of downloaded packets per second, proportion of the number of uploaded or downloaded packets, mean/variance of packet size and 25% segmentation point, packet length distribution frequency sequence, packet length transfer matrix, mean/variance of uploaded packet size and 25% segmentation point, mean/variance of downloaded packet size and 25% segmentation point, upload packet length distribution frequency sequence, download packet length distribution frequency sequence, upload packet length transfer matrix, download packet length transfer matrix, packet time interval mean/variance and 25% segmentation point, packet time interval distribution, upload packet time interval mean/variance and 25% segmentation point, download packet time interval mean/variance and 25% segmentation point, Upload packet time interval, download packet time interval, variance/kurtosis and skewness of the traffic sequence, variance/kurtosis and skewness of the upload traffic sequence, and variance/kurtosis and skewness of the download traffic sequence.
As a specific embodiment, in the streaming data security vulnerability traceability system in a video conference scenario, the scoring module 207 specifically includes:
a scoring unit for utilizing a formula
Scoring each feature group in the feature group set of the current detection to obtain a scoring vector of the current detection
,
(ii) a Wherein,
for the first in the set of currently detected feature groups
iA score for the individual feature set;
representing the recall ratio of the classification model;
representing the precision of the classification model.
As a specific embodiment, in the streaming data security vulnerability traceability system in a video conference scenario, the security vulnerability determining module 2010 specifically includes:
and the feature group screening unit is used for screening the T feature groups with the highest scoring results.
And the stream data characteristic determining unit is used for determining a plurality of stream data characteristics corresponding to each characteristic group in the T characteristic groups to obtain a stream data characteristic set.
And the security vulnerability determining unit is used for determining the stream data feature with the largest occurrence frequency in the stream data feature set as the stream data feature corresponding to the security vulnerability of the tth function module to be tested.
The embodiments in the present description 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. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.