CN111027623A - Data-enhanced intelligent terminal security level classification method and system - Google Patents

Data-enhanced intelligent terminal security level classification method and system Download PDF

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Publication number
CN111027623A
CN111027623A CN201911258944.8A CN201911258944A CN111027623A CN 111027623 A CN111027623 A CN 111027623A CN 201911258944 A CN201911258944 A CN 201911258944A CN 111027623 A CN111027623 A CN 111027623A
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intelligent terminal
level
security level
data
terminal
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罗伟峰
邓巍
文红
廖润发
张之涵
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University of Electronic Science and Technology of China
Shenzhen Power Supply Bureau Co Ltd
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University of Electronic Science and Technology of China
Shenzhen Power Supply Bureau Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
    • G06F21/577—Assessing vulnerabilities and evaluating computer system security
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/2113—Multi-level security, e.g. mandatory access control

Abstract

The invention relates to the technical field of communication safety, and discloses a data-enhanced intelligent terminal safety level classification method and system. The method comprises the following steps: acquiring a test data set of the intelligent terminal; injecting corresponding labels into the test data set to obtain an output sample set; constructing a new input channel information sample, carrying out average data enhancement to obtain an input sample set, carrying out average sample construction on a label matrix to obtain a new output sample set, and thus obtaining a new data set; taking the new data set as a training set, and training a safety class classifier; and carrying out security level classification on the intelligent terminal by using a security level classifier. The method constructs a new pseudo-evaluation sample by utilizing the correlation of the terminal security evaluation sample, and introduces a concept of random weight to increase the randomness of the sample construction so as to enhance the robustness of the sample set. The method can be applied to the enhancement of various AI-based terminal security level classifier data.

Description

Data-enhanced intelligent terminal security level classification method and system
Technical Field
The invention relates to the technical field of communication safety, in particular to a data-enhanced intelligent terminal safety level classification method and a data-enhanced intelligent terminal safety level classification system.
Background
With the popularization of networks and the development of 4G/5G wireless networks, the application of intelligent terminals (hereinafter referred to as terminals) goes deep into the daily life of people, and the intelligent terminals are widely applied to industries, transportation, medical treatment, cities and the like. Compared with a PC (personal computer), the terminal is limited in size and energy and computing capacity, and the terminal is widely distributed in various application scenes, is easy to approach and is more easily attacked. Along with the deepening of the dependence degree of people on the intelligent terminal, the safety problem of the terminal is increasingly highlighted. Especially, the functions of the terminal are more and more, the combination of the terminal and the internet is more and more compact, for example, third-party software such as a mobile online shopping platform, a mobile banking and chat software which is closely related to the property privacy of people is more and more, so that a user stores property information, personal privacy, business confidential documents and the like in the terminal. Recently, various attacks on the mobile intelligent terminal are endless, the attack on the terminal becomes an entry point for attacking the network, and the potential safety hazard of the terminal becomes an important problem of network safety. Therefore, it is very necessary to evaluate the security of the smart terminal.
In the safety evaluation of the mobile intelligent terminal, scientific quantification is carried out according to the results of each safety single test of each mobile intelligent terminal, the terminal safety grade is divided according to a certain evaluation basis, the important basis of different application scenes and different users on different safety requirements of the mobile intelligent terminal can be realized, and the safe use of different applications of the terminal with different safety grade requirements is realized. The mobile intelligent terminal safety evaluation becomes one of the most effective means for ensuring the safe use of the terminal, the grading of the terminal safety level is scientifically carried out according to various test results in the mobile intelligent terminal safety evaluation, the mobile intelligent terminal safety evaluation is an important criterion for the safety requirements of different groups and different individuals on the mobile intelligent terminal, and the accurate evaluation can realize the safe use of the requirements of different safety levels.
At present, methods for quantitatively dividing the security level of a terminal and testing each security unit of the terminal have certain achievements, then quantitative data of the security performance of the terminal are obtained through synthesis, an advanced classification method is adopted for carrying out security classification on the terminal, and particularly, the security performance of the terminal is objectively classified through a learning algorithm based on an Artificial Intelligence (AI) technology. However, the classification method based on the Artificial Intelligence (AI) technology requires a large amount of data to train the model, the time for testing the data is long, and the problem of inaccurate classification precision caused by insufficient data exists.
Disclosure of Invention
The invention aims to provide a data enhancement method and a data enhancement system for intelligent terminal security level classification, which aim to solve the problems that the classification precision is not accurate enough due to long data testing time and insufficient data of the classification method based on the artificial intelligence technology.
In order to achieve the above object, a first aspect of the present invention provides a method for classifying security levels of a data-enhanced intelligent terminal, where the method includes:
s1) acquiring a test data set of the intelligent terminal;
s2) injecting corresponding labels into the test data set to obtain an output sample set;
s3) constructing new input channel information samples from the output sample set;
s4) carrying out average data enhancement on the new input channel information sample to obtain an input sample set;
s5) carrying out label matrix after average sample construction on the input sample set to obtain a new output sample set;
s6) obtaining a new data set according to the input sample set and the new output sample set;
s7) taking the new data set as a training set, and training a safety class classifier;
s8) carrying out safety level classification on the intelligent terminal by using the safety level classifier based on the artificial intelligence model.
Further, step S1) obtains a test data set of the intelligent terminal, including:
s11) testing the kth intelligent terminal S times to obtain a test result
Figure BDA0002311077810000021
Each test result is composed of scores of n test singletons, namely vectors
Figure BDA0002311077810000031
Is represented by the formula (I) in which mjScore for jth test case;
s12), multiplying each test result by a weight function H (n) of a single case to obtain a total score Y of the intelligent terminal, wherein the weight function H (n) is a uniform probability density function and is expressed as H ═ H1,h2,…,hS]T，
Figure BDA0002311077810000032
Namely, it is
Figure BDA0002311077810000033
Meanwhile, the security level y of the intelligent terminal is divided into W levels, and W-1 threshold values are set to be positive numbers η1,η2,…,ηW-1When it satisfies 0<Y≤η1Then define the terminal security level as level 1, when η is satisfied1<Y≤η2Then the security level is defined as level 2, and so on, when η is satisfiedW-2<Y≤ηW-1Defining the terminal security level as K-1 level, and satisfying Y>ηW-1Defining the security level as W level;
s13) total score M calculated through intelligent terminaliAnd testing the security level y to obtain an S-time test data set D of the kth intelligent terminalk：
Dk:Dk＝{Xk,Yk}，
Wherein
Figure BDA0002311077810000034
Wherein T { (M)1,y1),(M2,y2),…,(MN,yN)}，yi∈{1,2,3,4}，i＝1,2,…,N。
Further, the output sample set in step S2) is:
Figure BDA0002311077810000035
wherein y isk∈{1,2,…,W}。
Further, the new input channel information sample in step S3) is:
Figure BDA0002311077810000036
α therein0Is a positive integer representing the number of samples constructed for each parametric evaluation sample.
Further, the input sample set in step S4) is:
Figure BDA0002311077810000041
wherein N iskRepresenting the number of channel information vectors after average data enhancement;
step S5) the new output sample set is:
Figure BDA0002311077810000042
the new data set in step S6) is:
Figure BDA0002311077810000043
further, step S8) of performing security level classification on the intelligent terminal by using the security level classifier based on the artificial intelligence model, including: and calculating the safety level of the intelligent terminal by adopting a W-1 layer support vector machine model according to the level W of the safety level.
Further, the method for calculating the security level of the intelligent terminal comprises the following steps:
s81), initializing the initial variable m to 1;
s82) dividing the training set into two types, wherein one type is y ═ m, and the other type is y ═ m + 1-W, and obtaining the training set
Figure BDA0002311077810000044
Wherein
Figure BDA0002311077810000045
Wherein the content of the first and second substances,
Figure BDA0002311077810000046
s83) constructing and solving a constrained optimization problem, the formula is as follows:
Figure BDA0002311077810000047
Figure BDA0002311077810000048
finding the optimal solution
Figure BDA0002311077810000049
In the formula (I), the compound is shown in the specification,
Figure BDA00023110778100000410
is a Lagrange multiplier vector, xi∈χ＝Rn，yi∈γ＝{+1,-1}，i＝1,2,3,…,S+Nk；
S84) calculating the normal magnitude of the hyperplane:
Figure BDA0002311077810000051
in the formula, w represents the normal magnitude of the classification hyperplane in the high-dimensional space;
at the same time, select α(m)A positive component of
Figure BDA0002311077810000052
Calculating the intercept value of the hyperplane:
Figure BDA0002311077810000053
in the formula, b represents an intercept value of a classification hyperplane in a high-dimensional space;
s85) calculating a hyperplane:
Figure BDA0002311077810000054
by means of a classification decision function:
Figure BDA0002311077810000055
identifying a terminal with a security level of m:
when f is(1)(Mi) When the terminal is 1, the security level of the terminal is m level;
when f is(1)(Mi) When the terminal is equal to-1, the security level of the terminal is m + 1-W;
s86) determining whether the value of m is equal to W-1:
if yes, finishing all safety grade grading;
if not, perform operation +1 on m, and go to step S82).
The second aspect of the present invention provides a data-enhanced intelligent terminal security level classification system, including:
the testing module is used for testing the intelligent terminal to obtain a testing data set;
the data enhancement module is used for injecting corresponding labels into the test data set to obtain an output sample set; constructing a new input channel information sample according to the output sample set; carrying out average data enhancement on the new input channel information sample to obtain an input sample set; carrying out label matrix after average sample construction on the input sample set to obtain a new output sample set; obtaining a new data set according to the input sample set and the new output sample set;
the model training module is used for taking the new data set as a training set and training a safety class classifier;
and the classification module is used for classifying the safety level of the intelligent terminal by using the safety level classifier based on the artificial intelligence model.
Further, the testing the intelligent terminal to obtain a test data set includes:
testing the intelligent terminal for multiple times to obtain a test result, wherein the test result consists of the score of at least one test single item;
multiplying the test result by a weight function of a single case to obtain a total calculation score of the intelligent terminal, and defining the safety level of the intelligent terminal;
and obtaining a test data set of the intelligent terminal according to the calculated total score and the safety level of the intelligent terminal.
Further, the performing, by using the security level classifier, security level classification on the intelligent terminal based on the artificial intelligence model includes: and calculating the safety level of the intelligent terminal by adopting a W-1 layer support vector machine model according to the level W of the safety level.
According to the technical scheme, the test data set of the intelligent terminal is obtained, a new pseudo-evaluation sample is constructed by utilizing the correlation of the terminal safety evaluation sample, and the randomness of the sample construction is increased by introducing the concept of random weight so as to enhance the robustness of the sample set. The data set enhancement method of the technical scheme of the invention is suitable for enhancing data of various AI-based terminal security level classifiers. The data set enhancement method for safety class classification provided by the invention is applicable to various intelligent terminal devices and has strong portability.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a flowchart of a method for classifying security levels of a data-enhanced intelligent terminal according to an embodiment of the present invention;
fig. 2 is a block diagram of a data-enhanced intelligent terminal security level classification system according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart of a method for classifying security levels of a data-enhanced intelligent terminal according to an embodiment of the present invention. As shown in fig. 1, a method for classifying security levels of a data-enhanced intelligent terminal according to an embodiment of the present invention includes the following steps:
s1) obtaining a test data set of the intelligent terminal.
The step comprises the following substeps:
s11) testing the kth intelligent terminal S times to obtain a test result
Figure BDA0002311077810000071
Each test result is composed of scores of n test singletons, namely vectors
Figure BDA0002311077810000072
Is represented by the formula (I) in which mjFor the score of the jth test case, a higher score indicates better safety performance. For example, the test list comprises a short message function, a call function, third-party software, kernel bugs, an audit function, and file storage and deletion warnings.
S12), multiplying each test result by a weight function H (n) of a single case to obtain a total score Y of the intelligent terminal, wherein the weight function H (n) is a uniform probability density function and is expressed as H ═ H1,h2,…,hS]T，
Figure BDA0002311077810000073
Namely, it is
Figure BDA0002311077810000074
Meanwhile, the security level y of the intelligent terminal is divided into W levels, and W-1 threshold values are set to be positive numbers η1,η2,…,ηW-1When it satisfies 0<Y≤η1Then define the terminal security level as level 1, when η is satisfied1<Y≤η2Then the security level is defined as level 2, and so on, when η is satisfiedW-2<Y≤ηW-1Defining the terminal security level as K-1 level, and satisfying Y>ηW-1Then, the security level is defined as W level, and a higher security level indicates that the terminal is more secure.
S13) total score M calculated through intelligent terminaliAnd testing the security level y to obtain an S-time test data set D of the kth intelligent terminalk：
Dk:Dk＝{Xk,Yk}，
Wherein
Figure BDA0002311077810000081
Wherein T { (M)1,y1),(M2,y2),…,(MN,yN)}，yi∈{1,2,3,4}，i＝1,2,…,N。
S2) injecting corresponding labels into the test data set to obtain an output sample set.
The output sample set is:
Figure BDA0002311077810000082
wherein y isk∈{1,2,…,W}。
S3) constructing new input channel information samples from the set of output samples.
The new input channel information samples are:
Figure BDA0002311077810000083
α therein0Is a positive integer representing the number of samples constructed for each parametric evaluation sample.
S4) carrying out average data enhancement on the new input channel information sample to obtain an input sample set.
The input sample set is:
Figure BDA0002311077810000084
wherein N iskIndicating the number of channel information vectors after mean data enhancement.
S5) carrying out average sample construction on the input sample set to obtain a new output sample set.
The new set of output samples is:
Figure BDA0002311077810000091
s6) obtaining a new data set from the input sample set and the new output sample set.
The new data set is:
Figure BDA0002311077810000092
s7) taking the new data set as a training set, and training a safety level classifier.
S8) carrying out safety level classification on the intelligent terminal by using the safety level classifier based on the artificial intelligence model.
According to the grade W of the security grade, a W-1 layer support vector machine model is adopted to calculate the security grade of the intelligent terminal, and the method comprises the following substeps:
s81) and making the initial variable m equal to 1.
S82) dividing the training set into two types, wherein one type is y ═ m, and the other type isM + 1-W to obtain the training set
Figure BDA0002311077810000093
Wherein
Figure BDA0002311077810000094
Wherein the content of the first and second substances,
Figure BDA0002311077810000095
s83) constructing and solving a constrained optimization problem, the formula is as follows:
Figure BDA0002311077810000096
Figure BDA0002311077810000097
finding the optimal solution
Figure BDA0002311077810000098
In the formula (I), the compound is shown in the specification,
Figure BDA0002311077810000099
is a Lagrange multiplier vector, xi∈χ＝Rn，yi∈γ＝{+1,-1}，i＝1,2,3,…,S+Nk。
S84) calculating the normal magnitude of the hyperplane:
Figure BDA0002311077810000101
in the formula, w represents the normal magnitude of the classification hyperplane in the high-dimensional space;
at the same time, select α(m)A positive component of
Figure BDA0002311077810000102
Calculating the intercept value of the hyperplane:
Figure BDA0002311077810000103
in the formula, b represents an intercept value of the classification hyperplane in the high-dimensional space.
S85) calculating a hyperplane:
Figure BDA0002311077810000104
by means of a classification decision function:
Figure BDA0002311077810000105
identifying a terminal with a security level of m:
when f is(1)(Mi) When the terminal is 1, the security level of the terminal is m level;
when f is(1)(Mi) And when the terminal is equal to-1, the terminal security level is m + 1-W.
S86) determining whether the value of m is equal to W-1:
if yes, finishing all safety grade grading;
if not, perform operation +1 on m, and go to step S82).
For example, the number of security levels is 4, and 3 threshold values η are set in step S12)1,η2,η3The 3-layer support vector machine model is adopted in step S8).
Fig. 2 is a block diagram of a data-enhanced intelligent terminal security level classification system according to an embodiment of the present invention. As shown in fig. 2, the data-enhanced intelligent terminal security class classification system provided by the embodiment of the present invention includes a test module, a data enhancement module, a model training module, and a classification module.
The test module is used for testing the intelligent terminal to obtain a test data set. Specifically, the method comprises the following steps: testing the intelligent terminal for multiple times to obtain a test result, wherein the test result consists of the score of at least one test single item; multiplying the test result by a weight function of a single case to obtain a total calculation score of the intelligent terminal, and defining the safety level of the intelligent terminal; and obtaining a test data set of the intelligent terminal according to the calculated total score and the safety level of the intelligent terminal.
The data enhancement module is used for injecting corresponding labels into the test data set to obtain an output sample set; constructing a new input channel information sample according to the output sample set; carrying out average data enhancement on the new input channel information sample to obtain an input sample set; carrying out label matrix after average sample construction on the input sample set to obtain a new output sample set; and obtaining a new data set according to the input sample set and the new output sample set.
And the model training module is used for taking the new data set as a training set to train the safety class classifier.
The classification module is used for classifying the safety level of the intelligent terminal by using the safety level classifier based on an artificial intelligence model. For example: and calculating the safety level of the intelligent terminal by adopting a W-1 layer support vector machine model according to the level W of the safety level.
The embodiment of the invention also provides a machine-readable storage medium, wherein the machine-readable storage medium stores computer program instructions, and the computer program instructions are executed by a processor to realize the data-enhanced intelligent terminal security level classification method.
Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be implemented by a program, which is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps in the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
While the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solution of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications are within the scope of the embodiments of the present invention. In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as disclosed in the embodiments of the present invention as long as it does not depart from the spirit of the embodiments of the present invention.

Claims (10)

1. A data-enhanced intelligent terminal security level classification method is characterized by comprising the following steps:
s1) acquiring a test data set of the intelligent terminal;
s2) injecting corresponding labels into the test data set to obtain an output sample set;
s3) constructing new input channel information samples from the output sample set;
s4) carrying out average data enhancement on the new input channel information sample to obtain an input sample set;
s5) carrying out label matrix after average sample construction on the input sample set to obtain a new output sample set;
s6) obtaining a new data set according to the input sample set and the new output sample set;
s7) taking the new data set as a training set, and training a safety class classifier;
s8) carrying out safety level classification on the intelligent terminal by using the safety level classifier based on the artificial intelligence model.
2. The method for classifying security levels of an intelligent terminal with enhanced data according to claim 1, wherein the step S1) of obtaining a test data set of the intelligent terminal comprises:
s11) testing the kth intelligent terminal S times to obtain a test result
Figure FDA0002311077800000011
Each test result is composed of scores of n test singletons, namely vectors
Figure FDA0002311077800000012
Is represented by the formula (I) in which mjScore for jth test case;
s12), multiplying each test result by a weight function H (n) of a single case to obtain a total score Y of the intelligent terminal, wherein the weight function H (n) is a uniform probability density function and is expressed as H ═ H1,h2,…,hS]T，
Figure FDA0002311077800000013
Namely, it is
Figure FDA0002311077800000014
Meanwhile, the security level y of the intelligent terminal is divided into W levels, and W-1 threshold values are set to be positive numbers η1,η2,…,ηW-1When it satisfies 0<Y≤η1Then define the terminal security level as level 1, when η is satisfied1<Y≤η2Then the security level is defined as level 2, and so on, when η is satisfiedW-2<Y≤ηW-1Defining the terminal security level as K-1 level, and satisfying Y>ηW-1Defining the security level as W level;
s13) total score M calculated through intelligent terminaliAnd testing the security level y to obtain an S-time test data set D of the kth intelligent terminalk：
Dk:Dk＝{Xk,Yk}，
Wherein
Figure FDA0002311077800000021
Wherein T { (M)1,y1),(M2,y2),…,(MN,yN)}，yi∈{1,2,3,4}，i＝1,2,…,N。
3. The method for classifying security levels of a data-enhanced intelligent terminal according to claim 2, wherein the output sample set in step S2) is:
Figure FDA0002311077800000022
wherein y isk∈{1,2,…,W}。
4. The method for classifying security levels of a data-enhanced intelligent terminal according to claim 3, wherein the new input channel information samples in step S3) are:
Figure FDA0002311077800000023
α therein0Is a positive integer representing the number of samples constructed for each parametric evaluation sample.
5. The data-enhanced intelligent terminal security level classification method according to claim 4,
the input sample set in step S4) is:
Figure FDA0002311077800000024
wherein N iskRepresenting the number of channel information vectors after average data enhancement;
step S5) the new output sample set is:
Figure FDA0002311077800000031
the new data set in step S6) is:
Figure FDA0002311077800000032
6. the data-enhanced intelligent terminal security level classification method according to claim 5, wherein the step S8) of performing security level classification on the intelligent terminal by using the security level classifier based on the artificial intelligence model comprises:
and calculating the safety level of the intelligent terminal by adopting a W-1 layer support vector machine model according to the level W of the safety level.
7. The data-enhanced intelligent terminal security level classification method according to claim 6, wherein the calculating of the intelligent terminal security level comprises the following steps:
s81), initializing the initial variable m to 1;
s82) dividing the training set into two types, wherein one type is y ═ m, and the other type is y ═ m + 1-W, and obtaining the training set
Figure FDA0002311077800000033
Wherein
Figure FDA0002311077800000034
Wherein the content of the first and second substances,
Figure FDA0002311077800000035
s83) constructing and solving a constrained optimization problem, the formula is as follows:
Figure FDA0002311077800000041
Figure FDA0002311077800000042
finding the optimal solution
Figure FDA0002311077800000043
In the formula (I), the compound is shown in the specification,
Figure FDA0002311077800000044
is a Lagrange multiplier vector, xi∈χ＝Rn，yi∈γ＝{+1,-1}，i＝1,2,3,…,S+Nk；
S84) calculating the normal magnitude of the hyperplane:
Figure FDA0002311077800000045
in the formula, w represents the normal magnitude of the classification hyperplane in the high-dimensional space;
at the same time, select α(m)A positive component of
Figure FDA0002311077800000046
Calculating the intercept value of the hyperplane:
Figure FDA0002311077800000047
in the formula, b represents an intercept value of a classification hyperplane in a high-dimensional space;
s85) calculating a hyperplane:
Figure FDA0002311077800000048
by means of a classification decision function:
Figure FDA0002311077800000049
identifying a terminal with a security level of m:
when f is(1)(Mi) When the terminal is 1, the security level of the terminal is m level;
when f is(1)(Mi) When the terminal is equal to-1, the security level of the terminal is m + 1-W;
s86) determining whether the value of m is equal to W-1:
if yes, finishing all safety grade grading;
if not, perform operation +1 on m, and go to step S82).
8. A data-enhanced intelligent terminal security level classification system is characterized by comprising:
the testing module is used for testing the intelligent terminal to obtain a testing data set;
the data enhancement module is used for injecting corresponding labels into the test data set to obtain an output sample set; constructing a new input channel information sample according to the output sample set; carrying out average data enhancement on the new input channel information sample to obtain an input sample set; carrying out label matrix after average sample construction on the input sample set to obtain a new output sample set; obtaining a new data set according to the input sample set and the new output sample set;
the model training module is used for taking the new data set as a training set and training a safety class classifier;
and the classification module is used for classifying the safety level of the intelligent terminal by using the safety level classifier based on the artificial intelligence model.
9. The system according to claim 8, wherein the testing the smart terminal to obtain a test data set comprises:
testing the intelligent terminal for multiple times to obtain a test result, wherein the test result consists of the score of at least one test single item;
multiplying the test result by a weight function of a single case to obtain a total calculation score of the intelligent terminal, and defining the safety level of the intelligent terminal;
and obtaining a test data set of the intelligent terminal according to the calculated total score and the safety level of the intelligent terminal.
10. The system according to claim 8, wherein the artificial intelligence model-based security level classification of the smart terminal using the security level classifier comprises:
and calculating the safety level of the intelligent terminal by adopting a W-1 layer support vector machine model according to the level W of the safety level.
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