CN105991517B - Vulnerability mining method and apparatus - Google Patents
Vulnerability mining method and apparatus Download PDFInfo
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Abstract
The present invention provides a kind of vulnerability mining method and apparatus.It is related to job control network safety filed；Solve the problems, such as that current testing scheme can not excavate the loophole that multiple input elements are introduced jointly.This method comprises: calculating the weight for causing the lopsided data of loophole；Construct multidimensional test case；The multidimensional test case is run, is that input is tested with the lopsided data；When loophole is arrived in excavation, the weight for causing the lopsided data of the loophole is set as maximum.Technical solution provided by the invention is suitable for FUZZ technology, realizes the discovery of multidimensional loophole.
Description
Technical field
The present invention relates to industrial control network security fields more particularly to a kind of vulnerability mining method and apparatus.
Background technique
Industrial control system based on computer technology and technical grade network has lasted the more than ten years, industrial control system
It is separated by pervious special equipment and with the external world and independent network system, developing into a kind of includes business administration
The industrial control system of layer, data acquisition information layer, Industry Control layer.This has merged the standard agreement of many IT field commercializations,
Such as Microsoft's Window operating system and Ethernet TCP/IP technology.This is saved greatly cost and improves work efficiency,
But a large amount of security risk is brought, such as platform loophole, network hole.To make industrial control system face dos attack, information
Leakage etc., so even more serious accident occurs.
For network, system safety, prevention is important more than protecting；It is particularly important for industrial control system.And for work
The bug excavation for controlling network protocol is the most important thing.
For bug excavation technology, wherein fuzz testing technology (FUZZ technology) occupies sizable ratio in bug excavation
Weight.
Currently based on the bug excavation technology of fuzz testing technology, there are several types of modes: the first is the skill based on generation
Art, core concept are to construct corresponding malformed protocol message according to the grammer, semanteme, synchronizing information of network protocol；Second
Kind is the technology based on variation；Its core concept is to capture original agreement message by corresponding tool or mode, is then existed
It modifies to constitute corresponding malformed protocol message on the basis of this；The third is the technology of agreement self study, technology master
It to be directed to unknown network technology, core concept is exactly to pass through cluster, message classification, message multisequencing pair to original agreement message
Than the methods of determine agreement grammatical and semantic etc., to construct corresponding malformed protocol message；4th kind is to be held based on symbol
Capable binary code bug excavation technology.Its core concept be exactly to binary code carry out dis-assembling, extract control stream and
Information is inputted, then lopsided data is inserted in input message part, stream information is controlled based on it and carries out static state on intermediate language
Semiology analysis.First three methods can be used for network protocol bug excavation.Later approach is exclusively used in the loophole of file type
It excavates.
In terms of Test cases technology, there are many methods generated, but current fuzz testing use-case generation technique is all
One-dimensional, i.e., only change an input element every time, and many loopholes are as caused by multiple input element collective effects.At present
Testing scheme can not excavate the loophole that multiple input elements are introduced jointly.
Summary of the invention
The present invention provides a kind of vulnerability mining method and apparatus, solve current testing scheme can not excavate it is multiple defeated
The problem of entering the loophole that element is introduced jointly.
A kind of vulnerability mining method, comprising:
Calculate the weight for causing the lopsided data of loophole；
Construct multidimensional test case；
The multidimensional test case is run, is that input is tested with the lopsided data；
When loophole is arrived in excavation, the weight for causing the lopsided data of the loophole is set as maximum.
Preferably, described the step of calculating the weight for causing the lopsided data of loophole, includes:
1) for the multitiered network that feedovers, a lesser non-zero random number is set for the weight coefficient Wij of each layer, wherein Wi,n+1
=-θ, every layer of multitiered network of the feedforward have n neuron, i=1,2 ..., n；J=1,2 ..., n；
2) a sample X=(X is inputted1,X2…Xn, 1), and corresponding desired output Y=(Y1,Y2…Yn)；
3) according to following formula, the output for i-th of neuron of kth layer is calculated
Wherein,Wi,n+1=-θ；
4) divide the learning error for separately calculating each layerInclude:
The learning error of output layer is calculated according to following formula,
The learning error of other each layers except output layer is calculated according to following formula:
5) according to following formula modified weight coefficient Wij and threshold values θ:
After having found out each layer each weight coefficient, it can discriminate whether to meet the requirements by the given index of quality；
Terminate to calculate when meeting the requirements, the return step 3 in backlog demand).
Preferably, for any given sample Xp=(Xp1,Xp2…Xpn, 1) and desired output Yp=(Yp1,Yp2…Ypn)
It is performed both by weight computing.
Preferably, the construction multidimensional test case includes:
Determine the major key of agreement to be tested；
Determine the fixed field of the agreement to be tested；
Determine the variable field of the agreement to be tested；
According to major key, fixed field and the variable field of the agreement to be tested, test case is constructed.
Preferably, according to major key, fixed field and the variable field of the agreement to be tested, constructing test case includes:
Either field is selected from the fixed field and the variable field, changes the value of the field, construction is directed to should
The one-dimensional test case of field.
Preferably, according to major key, fixed field and the variable field of the agreement to be tested, constructing test case includes:
Multiple fields are selected from the fixed field and the variable field, change the value of multiple field, construct needle
To the multidimensional test case of multiple field.
Preferably, the multidimensional test case is run, is to input test to include: with the lopsided data
Engine successively generates phase by the sequence of weight according to corresponding multidimensional test case, according to lopsided data at random
The lopsided data for meeting protocol format answered are sent to programmable logic controller (PLC) PLC and are tested.
Preferably, when loophole is arrived in excavation, the weight for causing the lopsided data of the loophole is set as after the step of maximum,
Further include:
Using the lopsided data, the bug excavation under different agreement is carried out.
The present invention also provides a kind of vulnerability mining devices, comprising:
Weight computing module, for calculating the weight for causing the lopsided data of loophole；
Use-case constructing module, for constructing multidimensional test case；
Test module is that input is tested with the lopsided data for running the multidimensional test case；
As a result execution module, for when loophole is arrived in excavation, the weight for causing the lopsided data of the loophole to be set as maximum.
The present invention provides a kind of vulnerability mining method and apparatus, calculate the weight for causing the lopsided data of loophole, construction
Multidimensional test case runs the multidimensional test case, is that input is tested with the lopsided data, arrives loophole excavating
When, the weight for causing the lopsided data of the loophole is set as maximum.The discovery for realizing multidimensional loophole solves current test
Scheme can not excavate the problem of loophole that multiple input elements are introduced jointly.
Detailed description of the invention
Fig. 1 is a kind of flow chart for vulnerability mining method that the embodiment of the present invention one provides；
Fig. 2 is the flow chart for constructing the weight for the lopsided data that BP network query function causes loophole；
Fig. 3 is that the embodiment of the present invention two provides a kind of structural schematic diagram of vulnerability mining device.
Specific embodiment
The embodiment of the present invention is described in detail below in conjunction with attached drawing.It should be noted that not conflicting
In the case of, the features in the embodiments and the embodiments of the present application can mutual any combination.
First in conjunction with attached drawing, the embodiment of the present invention one is illustrated.
The embodiment of the invention provides a kind of vulnerability mining methods, and process such as Fig. 1 of vulnerability mining is completed using this method
It is shown, comprising:
Step 101, calculating cause the weight of the lopsided data of loophole；
In this step, constructs lopsided data learning neural network and form background analysis platform.
There are many data often to cause loophole, such as " 0 ", ", ", " %^# $ " etc., is specifically carrying out multidimensional fuzz testing
When, how many lopsided data are just greater than according to weight height or weight and carry out fuzz testing, it is quick-fried that this addresses the problem associativities
Fried problem.
But at first initial weight is determined according to statistics or empirical value, not necessarily correctly, it is therefore desirable to establish one
The model and program, algorithm principle of BP net are as follows:
When back-propagation algorithm is applied to feedforward multitiered network, when using Sigmoid as excitation face number, following step can be used
Suddenly to the weight coefficient W of networkijRecurrence is carried out to seek.When paying attention to having n neuron for every layer, that is, there are i=1,2 ..., n；
J=1,2 ..., n.For i-th of neuron of kth layer, then there is n weight coefficient Wi1, Wi2..., Win, in addition take more-a Wi,n+1
For indicating threshold values θ i；And in input sample X, X=(X is taken1,X2…Xn,1)。
The step of execution of algorithm, is as shown in Figure 2, comprising:
(1) to weight coefficient WijSet initial value.
To the weight coefficient W of each layerijSet a lesser non-zero random number, but wherein Wi,n+1=-θ.
(2) a sample X=(X is inputted1,X2…Xn, 1), and corresponding desired output Y=(Y1,Y2…Yn)。
(3) output of each layer is calculated.
Output for i-th of neuron of kth layerHave:
Wi,n+1=-θ,
(4) learning error of each layer is sought
For output layer
For other each layers, have
(5) modified weight coefficient WijWith threshold values θ.
(6) it after having found out each layer each weight coefficient, can discriminate whether to meet the requirements by the given index of quality.If full
Foot requires, then algorithm terminates；If backlog demand, (3) execution is returned.
This learning process, for any given sample Xp=(Xp1,Xp2…Xpn, 1) and desired output Yp=(Yp1,
Yp2…Ypn) will execute, until meeting all input and output requirements.
By the learning algorithm of this BP neural network, the specific weight of each lopsided data has been determined that.
Step 102, construction multidimensional test case；
In this step, the tender spots of agreement is analyzed, the agreement of pending multidimensional fuzz testing is analyzed, to find more
It is possible that the field and field combination that go wrong can be generated process by test case in turn generates corresponding multidimensional test use
Example.
The construction of test case mainly follows following principle: 1, comprehensive；2, high efficiency；3, controllability.
Test case is stored in the form of a file, rather than is write in code, such controllability can be many by force.
It is a kind of combination of the description language of agreement for test case file, by it according to the relevant interface in lopsided library
A large amount of lopsided data are generated, in this way, disk space can be saved and working efficiency can be improved.
It is as follows for the construction process of test case: to determine major key, determine fixed field, determine that variable field, construction are single
Tie up test case, construction multidimensional test case
1) major key of agreement to be tested is determined.
Major key refers to a field the most key in a protocol test use-case construction, it is variable field certainly.Institute
Protocol test use-case all classify around it.By determine major key ensure that comprehensive and high efficiency at
It is possible.
2) fixed field of the agreement to be tested is determined.
In protocol massages, the value of some fields is fixed and invariable by protocol requirement, and field like this is just fixed
Field.
3) variable field of the agreement to be tested is determined.
Revocable is just variable field, and all fields can be seen as variable field in principle
4) according to major key, fixed field and the variable field of the agreement to be tested, test case is constructed；
Construction one-dimensional test case: so-called one-dimensional is exactly the value of only one protocol fields in transmitted protocol massages
It is variation, it is other all constant.
It constructs multidimensional test case: for many programs, only when a and b meet simultaneously, can just go to some point
The reason of propping up, and putting forward here it is multidimensional FUZZ, that is to say, that there are two above protocol fields in a test case
Value is variation, and such a test case is exactly multidimensional test case.
Step 103, the operation multidimensional test case are that input is tested with the lopsided data；
In this step, operation multidimensional test case carries out multidimensional FUZZING.
Engine is according to corresponding multidimensional test case, the deformity obtained according to BP deformity data learning neural network platform
Data successively generate the corresponding lopsided data for meeting protocol format by the sequence of weight at random and are sent to PLC.
Step 104, when excavating to loophole, the weight for causing the lopsided data of the loophole is set as maximum.
In this step, needs to adjust lopsided data weight and multidimensional fuzz testing is repeated.
If the excavation of certain fuzz testing springs a leak, the lopsided data of loophole are analyzed, then this are organized the power of lopsided data
Value is set as maximum, because there is the other parts of this bigger possible agreement also to have this loophole.Then it is directed to other tests again
Use-case carries out multidimensional fuzz testing, using the lopsided data, carries out the bug excavation under different agreement.
With reference to the accompanying drawing, the embodiment of the present invention two is illustrated.
The embodiment of the invention provides a kind of vulnerability mining device, structure is as shown in Figure 3, comprising:
Weight computing module 301, for calculating the weight for causing the lopsided data of loophole；
Use-case constructing module 302 constructs multidimensional test case for analyzing agreement tender spots based on the analysis results；
Test module 303 is that input is tested with the lopsided data for running the multidimensional test case；
As a result execution module 304, for when loophole is arrived in excavation, the weight for causing the lopsided data of the loophole to be set as most
Greatly.
The embodiment provides a kind of vulnerability mining method and apparatus, calculate the power for causing the lopsided data of loophole
Value constructs multidimensional test case, runs the multidimensional test case, is that input is tested with the lopsided data, is excavating
When to loophole, the weight for causing the lopsided data of the loophole is set as maximum.The discovery for realizing multidimensional loophole solves at present
Testing scheme the problem of can not excavating the loophole that multiple input elements are introduced jointly.
Those of ordinary skill in the art will appreciate that computer journey can be used in all or part of the steps of above-described embodiment
Sequence process realizes that the computer program can be stored in a computer readable storage medium, the computer program exists
(such as system, unit, device) executes on corresponding hardware platform, when being executed, include the steps that embodiment of the method it
One or combinations thereof.
Optionally, integrated circuit can be used also to realize in all or part of the steps of above-described embodiment, these steps can
To be fabricated to integrated circuit modules one by one respectively, or make multiple modules or steps in them to single integrated electricity
Road module is realized.In this way, the present invention is not limited to any specific hardware and softwares to combine.
Each device/functional module/functional unit in above-described embodiment, which can be adopted, is realized with general computing device realization, it
Can be concentrated on a single computing device, can also be distributed over a network of multiple computing devices.
Each device/functional module/functional unit in above-described embodiment realized in the form of software function module and as
Independent product when selling or using, can store in a computer readable storage medium.Computer mentioned above
Read/write memory medium can be read-only memory, disk or CD etc..
Anyone skilled in the art in the technical scope disclosed by the present invention, can readily occur in variation or
Replacement, should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with the guarantor described in claim
It protects subject to range.
Claims (8)
1. a kind of vulnerability mining method characterized by comprising
Calculate the weight for causing the lopsided data of loophole；
Agreement tender spots is analyzed, constructs multidimensional test case based on the analysis results；
The multidimensional test case is run, is that input is tested with the lopsided data；
When loophole is arrived in excavation, the weight for causing the lopsided data of the loophole is set as maximum,
It is described calculate cause loophole lopsided data weight the step of include:
It 1) is the weight coefficient W of each layer for the multitiered network that feedoversijA lesser non-zero random number is set, wherein Wi,n+1=-θ, institute
Stating every layer of multitiered network of feedforward has n neuron, i=1,2 ..., n；J=1,2 ..., n, i indicate i-th of nerve of kth layer
Member, j indicate j-th of weight coefficient of i-th of neuron for kth layer, Wi,n+1Indicate threshold θ i；
2) a sample X=(X is inputted1,X2…Xn, 1), and corresponding desired output Y=(Y1,Y2…Yn)；
3) according to following formula, the output for i-th of neuron of kth layer is calculated
Wherein,Wi,n+1=-θ；
4) learning error of each layer is calculated separatelyInclude:
The learning error of output layer is calculated according to following formula,
The learning error of other each layers except output layer is calculated according to following formula:
5) according to following formula modified weight coefficient WijAnd threshold θ:
After having found out each layer each weight coefficient, it can discriminate whether to meet the requirements by the given index of quality；
Terminate to calculate when meeting the requirements, the return step 3 in backlog demand).
2. vulnerability mining method according to claim 1, which is characterized in that
For any given sample Xp=(Xp1,Xp2…Xpn, 1) and desired output Yp=(Yp1,Yp2…Ypn) it is performed both by weight meter
It calculates.
3. vulnerability mining method according to claim 1, which is characterized in that the multidimensional test of construction based on the analysis results
Use-case constructs multidimensional test case
Determine the major key of agreement to be tested；
Determine the fixed field of the agreement to be tested；
Determine the variable field of the agreement to be tested；
According to major key, fixed field and the variable field of the agreement to be tested, test case is constructed.
4. vulnerability mining method according to claim 3, which is characterized in that according to the major key of the agreement to be tested, admittedly
Determine field and variable field, construction test case includes:
Either field is selected from the fixed field and the variable field, changes the value of the field, construction is directed to the field
One-dimensional test case.
5. vulnerability mining method according to claim 3, which is characterized in that according to the major key of the agreement to be tested, admittedly
Determine field and variable field, construction test case includes:
Multiple fields are selected from the fixed field and the variable field, change the value of multiple field, construction is directed to should
The multidimensional test case of multiple fields.
6. vulnerability mining method according to claim 1, which is characterized in that the multidimensional test case is run, with described
Lopsided data are to input test to include:
Engine is successively generated accordingly according to corresponding multidimensional test case, according to lopsided data by the sequence of weight at random
The lopsided data for meeting protocol format are sent to programmable logic controller (PLC) PLC and are tested.
7. vulnerability mining method according to claim 1, which is characterized in that when loophole is arrived in excavation, the loophole will be caused
The weights of lopsided data be set as after maximum step, further includes:
Using the lopsided data, the bug excavation under different agreement is carried out.
8. a kind of vulnerability mining device characterized by comprising
Weight computing module, for calculating the weight for causing the lopsided data of loophole；
Use-case constructing module constructs multidimensional test case for analyzing agreement tender spots based on the analysis results；
Test module is that input is tested with the lopsided data for running the multidimensional test case；
As a result execution module, for when loophole is arrived in excavation, the weight for causing the lopsided data of the loophole to be set as maximum,
Described calculate causes the weights of the lopsided data of loophole to include:
It 1) is the weight coefficient W of each layer for the multitiered network that feedoversijA lesser non-zero random number is set, wherein Wi,n+1=-θ, institute
Stating every layer of multitiered network of feedforward has n neuron, i=1,2 ..., n；J=1,2 ..., n, i indicate i-th of nerve of kth layer
Member, j indicate j-th of weight coefficient of i-th of neuron for kth layer, Wi,n+1Indicate threshold θ i；
2) a sample X=(X is inputted1,X2…Xn, 1), and corresponding desired output Y=(Y1,Y2…Yn)；
3) according to following formula, the output for i-th of neuron of kth layer is calculated
Wherein,Wi,n+1=-θ；
4) learning error of each layer is calculated separatelyInclude:
The learning error of output layer is calculated according to following formula,
The learning error of other each layers except output layer is calculated according to following formula:
5) according to following formula modified weight coefficient WijAnd threshold θ:
After having found out each layer each weight coefficient, it can discriminate whether to meet the requirements by the given index of quality；
Terminate to calculate when meeting the requirements, the return step 3 in backlog demand).
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| CN107025168B (en) * | 2015-12-15 | 2022-01-07 | 阿里巴巴集团控股有限公司 | Vulnerability detection method and device |
| CN106656657A (en) * | 2016-11-11 | 2017-05-10 | 北京匡恩网络科技有限责任公司 | Adaptive vulnerability mining framework based on industrial control protocol |
| CN106506280B (en) * | 2016-11-24 | 2019-10-01 | 工业和信息化部电信研究院 | The communication protocol test method and system of smart home device |
| CN106773719A (en) * | 2017-01-25 | 2017-05-31 | 上海云剑信息技术有限公司 | A kind of industrial control system leak automatic mining method based on BP neural network |
| CN106647612A (en) * | 2017-02-17 | 2017-05-10 | 上海云剑信息技术有限公司 | PLC vulnerability discovery method based on state relational map |
| CN107707553B (en) * | 2017-10-18 | 2020-02-07 | 北京启明星辰信息安全技术有限公司 | Weak password scanning method and device and computer storage medium |
| CN111427305B (en) * | 2020-03-29 | 2021-09-24 | 博智安全科技股份有限公司 | Method for Siemens PLC vulnerability mining |
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