CN108446848A - Individual networks awareness of safety scalar quantization evaluation method - Google Patents
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- CN108446848A CN108446848A CN201810233382.0A CN201810233382A CN108446848A CN 108446848 A CN108446848 A CN 108446848A CN 201810233382 A CN201810233382 A CN 201810233382A CN 108446848 A CN108446848 A CN 108446848A
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Abstract
For the problem that current network security realizes ambiguity in definition, lack effective assessment system and methodology, the present invention proposes individual networks awareness of safety scalar quantization evaluation method.This method includes three parts, subjective consciousness assessment, the assessment of behavior ontology and evaluation decision.Subjective consciousness evaluation part obtains individual information by questionnaire survey, is recognized to each information security by analytic hierarchy process (AHP), 4 information law ethics, information security knowledge, information security ability dimensions are given a mark, score of the acquisition individual consumer in this 4 dimensions.Behavior ontology evaluation part scans computer and mobile phone user's behavior, calculating and the optimal direct distance of user behavior, passes through random forests algorithm and differentiate user behavior safe class.Subjective consciousness assessment and the assessment of behavior ontology are merged, individual networks space safety level of consciousness is assessed.The experimental results showed that this method can realize individual networks awareness of safety scalar quantization in conjunction with machine learning method, granularity to weak from being not less than 4 grades by force, and Grading accuracy rate is 95% or more.This method can be used for improving the comprehensive and universality of individual networks awareness of safety scalar quantization model, have certain practical value.
Description
Technical field
The present invention relates to individual networks awareness of safety scalar quantization evaluation methods, belong to computer and information science technology neck
Domain.
Background technology
Existing awareness of network security appraisal procedure depends on subjective judgement and statistical method mostly.Factor of evaluation is single,
Lack objectivity；Simple statistics cannot portray structure feature complicated inside problem.For these problems, with information security
Based on basic theory, information attacking and defending game theory, assessed in conjunction with the safe Attack Defence method of up-to-date information and awareness of network security
Characteristic set builds the network security that a kind of assessment data dimension is wide, assessment content is comprehensive, assessment result has guiding value and anticipates
Know assessment models, assessment result is divided by weak multiple ranks, proposing individual networks awareness of safety scalar quantization evaluation method by force,
And crowd can be returned and carry out model verification.
Evaluation attribute (data type) involved by current network security consciousness assessment is not yet clear.Information security is one
The extremely extensive topic of scope, and the various security knowledges, principle, method involved by awareness of network security are same complicated numerous
It is trivial, it is therefore desirable to summarize extraction system, the major influence factors of comprehensive awareness of network security assessment, structure individual networks safety
Realize scalar quantization appraisement system, these are required for abundant information security research and working experience and bulk information safety
The excavation and analysis of data.The method mainly taken at present is divided into two kinds：Based on subjective intention analysis evaluation method and be based on
The evaluation method of behavior ontological analysis.
1. the appraisal procedure based on the analysis of subjective intention
Appraisal procedure based on the analysis of subjective intention is the main method of current awareness of network security assessment.It is pass through to
Measured sends questionnaire or the relevant information into test and appraisal acquisition user on line.Then to the information amount of progress of acquisition
Change.Quantization to information is actually a kind of process of determining evaluation criterion weight.By to individual acquisition relevant information into
Row weighted calculation is to obtain the evaluation result of comprehensive multidimensional.
Most common analysis method is analytic hierarchy process (AHP) in appraisal procedure based on the analysis of subjective intention.Such as 2013,
Dong Lipeng is using the safe and secret attainment of information of trainee as destination layer, and index is proposed as quasi- stratification according to Satty three times for design
1~9 proportion quotiety build the relative importance judgment matrix of indices by the way of expert judging.To calculate
The weight of each index.It scores each evaluating object, assessment awareness of network security is horizontal.Analytic hierarchy process (AHP) is to analysis personnel's
It is required that it is relatively high, and generally require the long time.On the other hand, micro-judgment is largely dependent upon by result
The conversed analysis of data obtains individual networks awareness of safety information.But human factor is subjective complicated, makes the cost of manual analysis
It becomes very large, it is most important that structure feature complicated inside data cannot be portrayed.
Scaling method is another important method of subjective intention analysis.Such as 2014, Gao Donghuai et al. proposes to use scale
To assess awareness of safety.Scale is worked out according to Likert scale, has " agreeing to very much " for each statement, " agreement ", " differ
Calmly ", " disagreeing ", " very different meaning " five kinds of answers, are denoted as 5 points, 4 points, 3 points, 2 points, 1 point respectively.Each surveyee's
Attitude total score is the summation of each problem score, and the attitude of the explainable surveyee of this total score is strong and weak or he is on this scale
Different conditions.Scaling method of grading is also defective, and using this scale, examination person is easy to generate haloing error and the middle error that becomes.
Everyone each project will be soon chosen as high score or average mark by excessively roomy or the golden mean of the Confucian school the examination person.Majority is commented
Grade scale is suitable for all units of tissue not for a certain special post, because without specific aim.Grading table
It is easy that the prejudice of examination person or halo effect is made to enter in evaluation.The structure of assessment models index system is asked dependent on investigation
Volume, it is subjective, objective behavial factor feature is had ignored, the objectivity and accuracy of model are affected.
2. the appraisal procedure of Behavior-based control ontology
Traditional analytic hierarchy process (AHP) based on questionnaire survey has comparable limitation.On the one hand it is easy to be carried out by user
Specific aim is coped with, it is difficult to reflect the true safety behavior of user.On the other hand acquisition information mode is single, it is difficult to which detection is different
User copes with security threat ability complicated and changeable under scene.The appraisal procedure of Behavior-based control ontology, by the visitor for acquiring user
Sight behavior, such as mobile phone uses, PC machine uses content of the act, objective evaluation awareness of safety is horizontal.
In conclusion existing assessment models also only consider the problems such as that there is only methods is single, assessment is comprehensive insufficient
To individual subjective consciousness without the importance in view of prior objective behavior ontological analysis, the present invention proposes individual net
Network awareness of safety scalar quantization evaluation method, by two aspect hierarchical layered amount of individual subjective consciousness and objective behavior ontology
Change, more granularity fusions build individual networks awareness of safety scalar quantization model.
Invention content
Present invention aim to address current network securities to realize ambiguity in definition, lacks asking for effective assessment system and methodology
Topic is unfolded to study to awareness of network security scalar quantization method, and in conjunction with various dimensions key factor, more granularity composite ratings calculate more
Method is mutually authenticated, and more real data are mutually examined, and proposes individual networks awareness of safety scalar quantization evaluation method, will assess object
Awareness of network security be divided into strong, stronger, general, weak four grades.
The design principle of the present invention includes three parts, subjective consciousness assessment, the assessment of behavior ontology and evaluation decision.It is subjective
Consciousness assessment part obtains individual information by questionnaire survey, is recognized to each information security by analytic hierarchy process (AHP), information law
Ethics, information security knowledge, the marking of information security ability four dimensions, score of the acquisition individual consumer in this four dimensions.Row
For ontology evaluation part, computer and mobile phone user's behavior, calculating and the optimal direct distance of user behavior are scanned, by random
Forest differentiates user behavior safe class.Finally fusion subjective consciousness assessment and the assessment of behavior ontology, assessment individual networks safety
Level of consciousness.
The technical scheme is that be achieved by the steps of：
Step 1, subjective consciousness is assessed.
Step 1.1, questionnaire survey obtains data.
Step 1.2, questionnaire survey data are for statistical analysis.
Step 1.3, analytic hierarchy process (AHP) calculates absolute weight of each layer element to target.
Step 2, behavior ontology is assessed.
Step 2.1, it is scanned by personal computer behavior scanning and mobile terminal behavior, it is relevant to obtain personal security's consciousness
Characteristic.
Step 2.2, the characteristic got is pre-processed, complies with the training sample requirement of random forest.
Step 2.3, using random forests algorithm to by pretreated personal computer and mobile terminal features sample
It is for statistical analysis.
Step 3, subjective and objective assessment, the evaluation of comprehensive classification quantized result.According to Subjective questionnaire on negative impact data and objective row
Decision model is established using tree for the attribute of ontology data, more algorithms are mutually authenticated, and more real data are mutually examined, and are obtained
Go out the scalar quantization result of individual networks awareness of safety.
Advantageous effect
Compared to the appraisal procedure analyzed based on subjective intention, the present invention can effectively excavate implicit between each index system
Related information also sufficiently lowers the influence of subjective factor, increases and is considered to objective behavial factor feature, greatly improves
The objectivity and accuracy of model.
Compared to the appraisal procedure of Behavior-based control ontology, the present invention fully takes into account the comprehensive and pervasive of key factor
Property, it takes the lead in combining machine learning algorithm, to evaluation result more multidimensional, the science of personal network's awareness of safety.
Description of the drawings
Fig. 1 is the schematic diagram of individual networks awareness of safety scalar quantization evaluation method of the present invention.
Fig. 2 is the awareness of network security evaluation index system to 1429 student's questionnaire surveys in specific implementation mode.
Specific implementation mode
In order to better illustrate objects and advantages of the present invention, the embodiment of the method for the present invention is done with reference to example
It is further described.
One is ground to 1429 student groups of Kenzo and doctor as experiment pair with Beijing Institute of Technology's information and electronics institute
As the determinant attribute number obtained in terms of three will be scanned from questionnaire survey and personal computer behavior scanning, mobile phone behavior respectively
According to as input, designing and dispose three tests：(1) 1429 are used by the determinant attribute data that Subjective questionnaire on negative impact obtains
AHP statistical analyses determine percentage contribution of lower layer's index to upper layer index using the feature vector for calculating judgment matrix；(2) right
Whether 2858 by comprising opening the PC end datas attribute of fire wall etc. and include the movement of application program danger permission quantity etc.
End data attribute carries out the processing of machine learning method；(3) decision tree is generated using the training of subjective and objective data, obtains last point
Grade quantized result, is divided into strong, stronger, general, weak four grades.
Detailed process is：
Step 1, questionnaire is carried out to Mailbox Of Technology University Of Beijing's breath and the student group of electronics institute Master degree candidate or more
Investigation, questionnaire are designed according to first class index and two-level index, each two-level index corresponds to twice topic, are divided into single choice, more
Four kinds of topic types are inscribed in the selected topic, True-False, analysis of cases, and table 1 shows 34 problem purpose particular contents.
1. questionnaire survey topic particular content of table
Step 2, analytic hierarchy process (AHP) weights, and each factor is resolved by different attribute at all levels, establishes hierarchical structure mould
Type compares the relative importance of each factor of same level in pairs, is configured to judgment matrix, using judgment matrix,
Relative weighting by comparison element to the last layer criterion is calculated using respective formula, and examines its consistency, if consistency
Pass through, that is, determines that weight needs to readjust judgment matrix if not passing through；Calculate absolute weight of each layer element to target；It comments
Estimate information security cognition, information knowledge, information security ethics, information security ability score.
Step 3, the personal computer of 1429 students is scanned, obtains more than 2000 computer safety informations.
Step 4, the individual mobile terminal information of 1429 students is scanned, obtains more than 1700 mobile terminal information securities
Data.
Step 5, feature is ranked up using the Importance of Attributes of random forests algorithm measurement, then uses sequence backward
Searching method removes the feature of one least important (importance score is minimum) from characteristic set, is gradually iterated every time,
And classification accuracy rate is calculated, finally obtain that attribute number is minimum, the highest characteristic set of classification accuracy rate is as feature selecting knot
Fruit finally extracts 9 dimension key influence factors of the ends PC personal security consciousness from 2,000 multidimensional information of acquisition, including whether
Open whether never expired, dangerous network interface card quantity of fire wall, user password etc..
Step 6, using the machine learning method of step 5, mutually independent key influence factor, structure network peace are filtered out
Full consciousness assessment characteristic set and quantitative expression method extract 6 dimension key influence factors of mobile terminal personal security consciousness, packet
Whether the permission of danger containing application program quantity, mobile phone are by root etc..
Step 7, by 4 dimension attributes of questionnaire survey (information security cognition, understanding, Legal ethics, ability) and computer, shifting
2 dimension attributes (key influence factor at the ends PC and mobile terminal) of moved end pass through decision tree, judgement assessment object information consciousness safety
The awareness of network security for assessing object is divided into strong, stronger, general, weak four grades by grade.
Test result：Experiment is based on individual networks awareness of safety scalar quantization model building method, by the letter of subjective questionnaire
It ceases Safety Cognition, understanding, Legal ethics, ability scoring and personal computer behavior scanning, mobile phone behavior and scans 6 dimensions
Consider, table 2 shows 9 key influence factors of Security of Personal Computer Net consciousness.
Table 3 shows 6 dimension key influence factors of individual mobile terminal awareness of network security.It is demonstrated experimentally that this is a kind of
The individual networks awareness of safety scalar quantization evaluation method that can be actually used, granularity to weak from being divided into 4 grades by force, Grading accuracy rate
95% or more.The method of the present invention takes in minute grade, and the pass in input data is effectively positioned with lower resource consumption
Key data and the influence that part extraneous data can be removed.
2. Security of Personal Computer Net of table realizes key influence factor table
3. individual mobile terminal awareness of network security key influence factor table of table
Above-described specific descriptions have carried out further specifically the purpose, technical solution and advantageous effect of invention
It is bright, it should be understood that the above is only a specific embodiment of the present invention, the protection model being not intended to limit the present invention
It encloses, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should be included in the present invention
Protection domain within.
Claims (4)
1. individual networks awareness of safety scalar quantization evaluation method, it is characterised in that described method includes following steps：
Step 1, subjective consciousness is assessed, and obtains data by questionnaire survey, and questionnaire survey data are for statistical analysis, then make
Absolute weight of each layer element to target is calculated with analytic hierarchy process (AHP)；
Step 2, behavior ontology is assessed, by personal computer behavior scanning and mobile terminal behavior scanning, obtaining personal security
Realize relevant characteristic；
Step 3, decision is established using tree according to the attribute of Subjective questionnaire on negative impact data and objective behavior ontology data
Model, more algorithms are mutually authenticated, and more real data are mutually examined, and obtain the scalar quantization result of individual networks awareness of safety.
2. according to the method described in claim 1, it is characterized in that, calculating each layer element to the exhausted of target using analytic hierarchy process (AHP)
To weight, specific steps include：
Step 1.1, questionnaire survey is carried out, questionnaire is designed according to first class index and two-level index, each two-level index corresponds to two
Road topic is divided into single choice, multiple choice, True-False, analysis of cases four kinds of topic types of topic, amounts to 34；
Step 1.2, analytic hierarchy process (AHP) weights, and each factor is resolved by different attribute at all levels, establishes hierarchy Model,
The relative importance of each factor of same level is compared in pairs, is configured to, to judgment matrix, using judgment matrix, use
Respective formula calculates the relative weighting to the last layer criterion by comparison element, and examines its consistency, if consistency passes through,
It determines that weight needs to readjust judgment matrix if not passing through, calculates absolute weight of each layer element to target, assessment letter
Cease Safety Cognition, information knowledge, information security ethics, information security ability score.
3. according to the method described in claim 1, it is characterized in that, assessed by behavior ontology, personal computer behavior is swept
It retouches and is scanned with mobile terminal behavior, obtain personal security and realize relevant characteristic, the characteristic got is located in advance
Reason, and using the data result of random forests algorithm acquisition personal computer and mobile terminal, step includes：
Step 2.1, personal computer is scanned, and obtains more than 2000 computer safety informations；
Step 2.2, individual mobile terminal information is scanned, obtains more than 1700 mobile terminal information security data；
Step 2.3, feature is ranked up using the Importance of Attributes of random forests algorithm measurement, is then searched backward using sequence
Suo Fangfa removes the feature of one least important (importance score is minimum) from characteristic set, is gradually iterated every time, and
Calculate classification accuracy rate, finally obtain attribute number is minimum, the highest characteristic set of classification accuracy rate as feature selecting as a result,
Extract the key influence factor of personal computer and mobile terminal.
4. according to the method described in claim 1, it is characterized in that, subjective and objective assessment comprehensive classification quantized result evaluate, according to
The attribute of Subjective questionnaire on negative impact data and objective behavior ontology data establishes decision model using tree, obtains individual net
The scalar quantization of network awareness of safety as a result, using questionnaire survey 4 dimension attributes (information security cognition, understanding, Legal ethics, energy
Power) and computer, mobile terminal 2 dimension attributes (key influence factor at the ends PC and mobile terminal) build decision tree, judgement assessment pair
Image information realizes safe class, and the awareness of network security for assessing object is divided into strong, stronger, general, weak 4 grades.
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| CN111370061A (en) * | 2019-06-20 | 2020-07-03 | 深圳思勤医疗科技有限公司 | Cancer screening method based on protein marker and artificial intelligence |
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| CN112766792A (en) * | 2021-01-29 | 2021-05-07 | 北京译泰教育科技有限公司 | Capacity tree creating method |
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