CN107220548A - A kind of system detecting method and system based on data slicer uniformity - Google Patents
A kind of system detecting method and system based on data slicer uniformity Download PDFInfo
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Abstract
A kind of system detecting method and system based on data slicer uniformity, including：Customer transaction behavior trace is obtained, and user's control flow model is built according to customer transaction behavior trace, and according to the transaction data of user, constructs the data flow model of the user；The behavior model for obtaining the user is integrated according to control flow model and data flow model；According to the section of user behavior and data characteristics analytical behavior model and data model, dynamic behavior section and data slicer are obtained, the overall section of customer transaction system of the analysis bag containing user behavior and data characteristics obtains data dependence information；The uniformity for the data cut into slices according to the behavior of data dependence infomation detection, obtains consistency detection result；User behavior and data exception are detected according to uniformity result, locking causes the transaction system weak section of data exception.
Description
Technical field
It is more particularly to a kind of to be based on data slicer uniformity the present invention relates to a kind of system vulnerability method for detecting area
System detecting method.
Background technology
With increasing for the online transaction enterprise such as " Taobao ", " Alibaba ", online transaction has become increasingly prevalent.
Consumer is traded by network, using the mode of network payment, has become a kind of important transaction row in people's life
For.It is a kind of new mode of doing business yet with e-payment, still among developing and improving, for the correlation of e-payment
Laws and regulations are also not perfect enough, and diversified factor makes the security of e-payment challenged.Many ecommerce are soft
The technology of part system is not mature enough and reliable, there is leak and mistake, is easily utilized by foreign invaders, so as to cause huge
Economic loss.The uniformity and robustness analysis of network trading flow is caused to be difficult to ensure that due to the presence of fragility, and it is crisp
Weak property can change with the situation of operation flow cooperation and component interaction.
Existing technology mainly carries out vulnerability analysis using static method, it is impossible to the change outside reply, so that
The fragility of system can not effectively be found.Two class technologies are roughly divided into from dynamic angle analysis fragility：On the one hand only in control
In terms of stream, set out with the dependency relation between user behavior, the dependence between research user behavior is traced to source to be found with this and is
The fragility of the controlling stream of system.It has ignored in process of exchange by the inconsistent caused system insecurity of data；And it is another
Technology only studies the data inconsistency of transaction system, and is not avoided that in transaction system because system is consolidated caused by controlling stream
Some fragility.
It is of the prior art that vulnerability analysis is carried out using static method, it is impossible to effectively to find the fragility of system.It is existing
There is dynamic approach only in terms of controlling stream, to be set out with the dependency relation between user behavior, research dependence, which traces to source to look for, is
The fragility of the controlling stream of system, have ignored the inconsistent caused system insecurity of data；Or only study the data of transaction system
Inconsistency, and it is not avoided that in transaction system because of the fragility that system caused by controlling stream is intrinsic there is dynamical system prison
Survey the technical problem of poor effect, non-data consistency detection change and controlling stream vulnerability checking missing.
The content of the invention
In view of the shortcoming of above prior art, based on data slicer uniformity it is it is an object of the invention to provide a kind of
Unite detection method and system, for solving, dynamic system monitoring effect in the prior art is poor, non-data consistency detection change
And the technical problem of controlling stream vulnerability checking missing.
In order to achieve the above objects and other related objects, the present invention provides a kind of system inspection based on data slicer uniformity
Method and system are surveyed, including：A kind of system detecting method based on data slicer uniformity, it is characterised in that including：Obtain and use
Family trading activity trace, and user's control flow model is built according to customer transaction behavior trace, and according to the transaction data of user
Information, constructs the data flow model of the user；The behavior mould for obtaining the user is integrated according to control flow model and data flow model
Type；According to the section of user behavior and data characteristics analytical behavior model and data model, dynamic behavior section and number are obtained
According to section, the overall section of customer transaction system of the analysis bag containing user behavior and data characteristics obtains data dependence information；Root
The uniformity for the data cut into slices according to the behavior of data dependence infomation detection, obtains consistency detection result；Detected according to uniformity result
User behavior and data exception, locking cause the transaction system weak section of data exception.
In one embodiment of the present invention, customer transaction behavior trace is obtained, and according to customer transaction behavior trace structure
User's control flow model is built, and according to the transaction data of user, constructs the data flow model of the user, including：From tested
The transaction data of system user is obtained in the running of examining system；The friendship of system user is obtained according to transaction data
Easy is tracking information；Trading activity tracking information is switched to trigger transition sequence；According to system user in monitored system
Transaction flow builds control flow model；Data flow model is built according to transaction data.
In one embodiment of the present invention, according to user behavior and data characteristics analytical behavior model and data model
Section, obtains dynamic behavior section and data slicer, customer transaction system of the analysis bag containing user behavior and data characteristics
Overall section, obtains data dependence information, including：When user's operation occurring in tested examining system, the transaction of the user is obtained
Data message；Conversion transaction data is data relationship information；Behavior characteristic information is obtained from transaction data；According to number
According to relation information and behavior characteristic information, the behavior model built by the user carries out slice analysis, obtains dynamic behaviour section；
Data behavioural characteristic in piece analytical behavior model, obtains data dependence information.
In one embodiment of the present invention, the uniformity for the data cut into slices according to the behavior of data dependence infomation detection is obtained
Consistency detection result, including：Obtain all triggering transition sequences and data dependence information；All triggering transition sequences are traveled through,
Final state transition sequence is determined whether according to behavior model and data dependence information；If so, then calculating the end according to logic of propositions
The corresponding data slicer of state transition sequence；If it is not, then whether all triggering transition sequences of cycle criterion are final state transition sequence；Obtain
Take final state transition sequence；Judge whether the corresponding data of the corresponding behavior section of final state transition sequence are consistent, obtain data consistency
Judged result.
In one embodiment of the present invention, user behavior and data exception are detected according to uniformity result, locking causes
The transaction system weak section of data exception, including：Obtain uniformity judged result；Final state is judged according to uniformity judged result
The corresponding data of the corresponding behavior section of transition sequence are consistent；If so, then all final state transition sequences of cycle criterion and its correspondingly
Data slicer；If it is not, then determining the appearance abnormal area in behavior model according to the corresponding arrival final state sequence of behavior section.
In one embodiment of the present invention, a kind of system detectio system based on data slicer uniformity, including：Model
Acquisition module, behavior model build module, slice analysis module, the consistent judge module of data and weak section detection module；Mould
Type acquisition module, user's control flow model is built for obtaining customer transaction behavior trace, and according to customer transaction behavior trace,
And according to the transaction data of user, construct the data flow model of the user；Behavior model builds module, for according to control
Flow model and data flow model integrate the behavior model for obtaining the user, and behavior model builds module and connected with model acquisition module
Connect；Slice analysis module, for the section according to user behavior and data characteristics analytical behavior model and data model, is moved
The behavior section of state and data slicer, the overall section of customer transaction system of the analysis bag containing user behavior and data characteristics, cut
Piece analysis module builds module with behavior model and is connected；The consistent judge module of data, for according to data dependence infomation detection row
For the uniformity of the data of section, consistency detection result is obtained, the consistent judge module of data is connected with slice analysis module；It is fragile
Region detection module, for detecting user behavior and data exception according to uniformity result, judges to cause the transaction of data exception
System weak section, weak section detection module is connected with data consistency judge module.
In one embodiment of the present invention, model acquisition module, including：Transaction data acquisition module, transaction trace are obtained
Modulus block, triggering transition sequence module, control flow model build module, data flow model and build module；Transaction data obtains mould
Block, the transaction data for obtaining system user from the running of tested examining system；Transaction trace acquisition module, is used
In the trading activity tracking information that system user is obtained according to transaction data, transaction trace acquisition module is obtained with transaction data
Modulus block is connected；Transition sequence module is triggered, for switching to trading activity tracking information to trigger transition sequence, triggering transition sequence
Row module is connected with transaction acquisition module；Control flow model builds module, for the friendship according to system user in monitored system
Easy flow builds control flow model, and control flow model builds module and is connected with transaction trace acquisition module, and control flow model is built
Module is connected with triggering transition sequence module；Data flow model builds module, for building data flow according to transaction data
Model, data flow model builds module and is connected with transaction data acquisition module.
In one embodiment of the present invention, slice analysis module, including：Operation information acquisition module, data relationship mould
Block, characteristic extracting module, dynamic behaviour section module, behavior model analysis module；Operation information acquisition module, for tested
When user's operation occurring in examining system, the transaction data of the user is obtained；Data relationship module, for converting transaction data
Information is data relationship information, and data relationship module is connected with operation information acquisition module；Characteristic extracting module, for from transaction
Data message obtains behavior characteristic information, and characteristic extracting module is connected with operation information acquisition module；Dynamic behaviour section module,
For according to data relationship information and behavior characteristic information, the behavior model built by the user to carry out slice analysis, obtained
State behavior is cut into slices, and dynamic behaviour section module is connected with data relationship module, dynamic behaviour section module and characteristic extracting module
Connection；Behavior model analysis module, for the data behavioural characteristic in slice analysis behavior model, obtains data dependence information,
Behavior model analysis module is connected with dynamic behaviour section module.
In one embodiment of the present invention, the consistent judge module of data, including：Transition acquisition module, combination final state are sentenced
Disconnected module, data slicer computing module, transition cycle criterion module, final state retrieval module and uniformity judge module；Become
Acquisition module is moved, for obtaining all triggering transition sequences and data dependence information；Final state judge module is combined, for traveling through
There is triggering transition sequence, final state transition sequence is determined whether according to behavior model and data dependence information, combination final state judges
Module is connected with transition acquisition module；Data slicer computing module, for trigger transition sequence be final state transition sequence when, root
The corresponding data slicer of final state transition sequence is calculated according to logic of propositions, data slicer computing module is with combining final state judge module
Connection；Cycle criterion module is changed, in the non-final state transition sequence of triggering transition sequence, all triggerings of cycle criterion to be changed
Whether sequence is final state transition sequence, and transition cycle criterion module is with combining the connection of final state judge module；Final state retrieval mould
Block, for obtaining final state transition sequence, final state retrieval module is with combining the connection of final state judge module；Uniformity judges mould
Block, for judging whether the corresponding data of the corresponding behavior section of final state transition sequence are consistent, obtain data consistency judged result,
Uniformity judge module is connected with final state retrieval module.
In one embodiment of the present invention, weak section detection module, including：Uniformity result acquisition module, final state
Sequence loops judge module and abnormal area identification module；Uniformity result acquisition module, for obtaining uniformity judged result；
Fragility judge module, for judging that behavior cuts into slices corresponding data unanimously according to uniformity judged result, fragility judges mould
Block is connected with uniformity result acquisition module；Final state sequence loops judge module, for consistent in the corresponding data of behavior section
When, all final state transition sequences of cycle criterion and its corresponding data slicer, final state sequence loops judge module are sentenced with fragility
Disconnected module connection；Abnormal area identification module, for when the corresponding data of behavior section are inconsistent, being cut into slices according to behavior corresponding
Arrival final state sequence determine appearance abnormal area in behavior model, abnormal area identification module connects with fragility judge module
Connect.
In summary, the present invention provides a kind of stream rule conflict detection method and system based on alias stipulations tree, this hair
A kind of system detecting method and system based on data slicer uniformity of bright offer, have the advantages that：Pass through user
Controlling behavior and the inconsistent detection of data, the security of transaction system is detected using microtomy so that transaction system
System disclosure satisfy that the uniformity and behavior congruence of fund, on the one hand can improve the robustness of transaction system, on the other hand right
Transaction system can be defendd in advance.The present invention is analyzed by the behavior to user first, while by analyzing user
Dependence between data, by the consistency analysis to transaction data and behavior, obtains the data slicer of uniformity, so that
To the reason for causing system exception, i.e. the fragility of system.The fragility that this patent studies system from Petri network microtome angle is asked
Topic, it is proposed that the transaction system detection method based on the section of fund and behavior unanimously, the matching solved in conventional art is wrong
Miss and the technical problem that rule action conflicts is flowed in wrong report.
Brief description of the drawings
Fig. 1 is shown as a kind of system detecting method step schematic diagram based on data slicer uniformity of the present invention.
Fig. 2 is shown as the control flow model and data flow model obtaining step schematic diagram of the present invention.
Fig. 3 is shown as the Dynamic Slicing analytical procedure schematic diagram of the present invention.
Fig. 4 is shown as the data consistency detecting step schematic diagram of the present invention.
Fig. 5 is shown as the weak section detecting step schematic diagram of the present invention.
Fig. 6 Trading Models change view.
Fig. 7 is shown as a kind of system detectio system module schematic diagram based on data slicer uniformity of the present invention.
Fig. 8 is shown as the model acquisition module schematic diagram of the present invention.
Fig. 9 is shown as the slice analysis module diagram of the present invention.
Figure 10 is shown as the consistent judge module schematic diagram of data of the present invention.
Figure 11 is shown as the weak section detection module schematic diagram of the present invention.
Component label instructions
The 1 system detectio system based on data slicer uniformity
11 model acquisition modules
12 behavior models build module
13 slice analysis modules
The consistent judge module of 14 data
15 weak section detection modules
111 transaction data acquisition modules
112 transaction trace acquisition modules
113 triggering transition sequence modules
114 control flow models build module
115 data flow models build module
131 operation information acquisition modules
132 data relationship modules
133 characteristic extracting modules
134 dynamic behaviours section module
135 behavior model analysis modules
141 transition acquisition modules
142 combination final state judge modules
143 data slicer computing modules
144 transition cycle criterion modules
145 final state retrieval modules
146 uniformity judge modules
151 uniformity result acquisition modules
152 fragility judge modules
153 final state sequence loops judge modules
154 abnormal area identification modules
Step numbers explanation
Fig. 1 S1~S5
Fig. 2 S11~S15
Fig. 3 S31~S35
Fig. 4 S41~S46
Fig. 5 S41~S44
Embodiment
Embodiments of the present invention are illustrated by particular specific embodiment below, those skilled in the art can be by this explanation
Content disclosed by book understands other advantages and effect of the present invention easily.
Fig. 1 is referred to Figure 11, it should however be clear that the structure depicted in this specification institute accompanying drawings, only to coordinate specification
Disclosed content, so that those skilled in the art is understood with reading, is not limited to enforceable restriction bar of the invention
Part, therefore do not have technical essential meaning, the modification of any structure, the change of proportionate relationship or the adjustment of size are not influenceing
Under effect that the utility model can be generated and the purpose that can reach, all should still it fall in the technology disclosed in the utility model
In the range of Rong Suoneng is covered.Meanwhile, in this specification it is cited as " on ", " under ", " left side ", " right side ", " centre " and " one "
Deng term, be merely convenient to understanding for narration, and be not used to limit enforceable scope of the invention, the change of its relativeness
Or adjustment, under without essence change technology contents, when being also considered as enforceable category of the invention.
Referring to Fig. 1, being shown as a kind of system detecting method step signal based on data slicer uniformity of the present invention
Figure, as shown in figure 1, including：A kind of system detecting method based on data slicer uniformity, including：
S1, acquisition customer transaction behavior trace, and user's control flow model, and root are built according to customer transaction behavior trace
According to the transaction data of user, the data flow model of the user is constructed, for the customer transaction behavior trace captured, is built
The transaction system of control and data flow based on PN machines；
S2, the behavior model for obtaining the user is integrated according to control flow model and data flow model, built based on BPMN's
User's control flow model, and according to customer transaction data message, the data flow model of structuring user's obtains the behavior of user with this
Model；
S3, the section according to user behavior and data characteristics analytical behavior model and data model, obtain dynamic behavior
Section and data slicer, the overall section of customer transaction system of the analysis bag containing user behavior and data characteristics, obtain data according to
Rely information；
S4, the data cut into slices according to the behavior of data dependence infomation detection uniformity, obtain consistency detection result, right respectively
Dependence and data dependence relation between the behavior of user are analyzed, and give detecting system controlling stream and data flow
Vulnerability analysis method, is analyzed by the behavior to user first, while by analyzing the dependence between user data,
By the consistency analysis to transaction data and behavior, the data slicer of uniformity is obtained；
S5, user behavior and data exception are detected according to uniformity result, locking causes the transaction system of data exception crisp
Weak-strong test, the fragility of the reason for obtaining causing system exception, i.e. system, the technology being capable of integrated system controlling stream and data flow
Information, and more accurately judged with this fragility to system, so as to be ready for defence in advance.
Referring to Fig. 2, the control flow model and data flow model obtaining step schematic diagram of the present invention is shown as, such as Fig. 2 institutes
Show, S1, obtain customer transaction behavior trace, and according to customer transaction behavior trace build user's control flow model, and according to
The transaction data at family, constructs the data flow model of the user, including：
S11, the transaction data for obtaining from the running of tested examining system system user, are used as a kind of promotion hand
Section, in recent years, day cat store release " birthday gift bag ", and member gives in week birthday (birthday the first six day and on the day of) purchase commodity
Double integration.So-called " double " refers to, except obtaining integrating with the shopping of identical in the past, also enjoys and being accumulated with shopping during purchase commodity
The birthday integration for value of grading.The privilege of birthday gift bag is, if returning goods, and only returns shopping integration, and buyer still possesses the birthday
Integration；
S12, the trading activity tracking information according to transaction data acquisition system user, are captured first against each
Customer transaction behavior trace, be all the step of whole " wash sale, gain integration, integration by cheating cash " closed at one be
Completed in system, land et al. is using 6 days cat shop of actual control, and " buyer " and " seller " are played the part of in this 6 shops simultaneously
Role.In this 6 shops, they have been made during different virtual goodses links, each goods links respective operations
Each step：The activation birthday is franchise, gain integration by cheating, integration is cashed.Process substantially is, by activating birthday privilege, to buy
Family's account obtains birthday integration；Another virtual goodses then are bought in actually other shops of control, are paid the bill with integration deduction,
Integration has also reformed into cash, gets in the account of seller et al.；
S13, by trading activity tracking information switch to trigger transition sequence, whereinP ' ∪ T '=
slice(Mn, σ, Qc), P ' P, T ' T, and F '=F |(P ', T '), wherein P ' is state set, and T ' is triggering transition set, MnFor
Mark and constraint, σ are transition sequence, and F is controlling stream set；
S14, the transaction flow structure control flow model according to system user in monitored system,(1000 interchangeable 1 yuan of integration), g=g (z, s)=z+ ω s can be translated into label Petri
Pessimistic concurrency control, x represents the amount of money, and y represents integration, and s is the price of each commodity, and ω is weights；
S15, data flow model built according to transaction data, with according to dealing money, integration and commodity price etc. are handed over
Easy data build data flow model.
Referring to Fig. 3, be shown as the present invention Dynamic Slicing analytical procedure schematic diagram, as shown in figure 3, S3, according to user
Behavior and the section of data characteristics analytical behavior model and data model, obtain dynamic behavior section and data slicer, analysis
The overall section of customer transaction system comprising user behavior and data characteristics, obtains data dependence information, including：
S31, when occurring user's operation in tested examining system, obtain the transaction data of the user；
S32, conversion transaction data be data relationship information, the data relationship information be behavior model in data according to
Rely information；
S33, from transaction data obtain behavior characteristic information, extract dealing money, the net in transaction data
The behavior characteristic informations such as upper trading account integration, the price of tradable commodity；
S34, according to data relationship information and behavior characteristic information, the behavior model built by the user carries out section point
Analysis, obtains dynamic behaviour section, makes N=(P, T, F), be a Petri network and make<M0, Q>, it is a N section standard；Cut
Piece slice meets condition：For each sequence σ=t2Ltn,So that Mn-1(p) ＜ Mn(p), for certain
Individual Qc and<N ', M '0>, wherein presence one occurs sequence σ ' and σ ' is σ subsequence；M≤n and M 'mCover
M is coveredn|P′(i.e. M 'm≥Mn|P′), wherein M is mark and constrains, and σ is transition sequence, and F is controlling stream set；
Data behavioural characteristic in S35, slice analysis behavior model, obtain data dependence information, for a sequence σ=
t2LtnIts Dynamic Slicing can be calculated<M0, σ, Q>It is as follows：N subnet N=(P, T, F), for all from M0To PqSequence σ1,
σ2..., σnIf,So output DCS=slice (Mi, C, Pq), M is mark and constrains, and σ is change
Sequence is moved, F is controlling stream set, and DCS cuts into slices for behavior.
Referring to Fig. 4, be shown as the present invention data consistency detecting step schematic diagram, as shown in figure 4, S4, according to number
The uniformity of the data of behavior section is detected according to Dependency Specification, consistency detection result is obtained, including：
S41, all triggering transition sequences of acquisition and data dependence information；
S42, all triggering transition sequences of traversal, final state transition are determined whether according to behavior model and data dependence information
Sequence, ifAnd only exist a sequence σ1Reach Pq, calculate σiIn triggering transition ti, then DCS
={ ti}∪slice(Mi, σi, Pq∪·ti)；Until finding the last item sequence σhSo thatAnd make σ=
σi∪σj∪σm∪L∪σh, calculate σhIn triggering transition th, DCS Dynamic Slicing result of calculations are exported, wherein P is state set,
T changes for triggering, and M is mark and constrains, and σ is transition sequence, and F is controlling stream set, and i, j, m are integer；
S43, if so, then calculate the corresponding data slicer of final state transition sequence according to logic of propositions, if can find another
An outer sequence σjSo thatAnd make σ=σi∪σj, output DCS=slice (Mi, σ, Pq) DCS=slice (Mi, σ,
Pq), data consistency can be tried to achieve according to Dynamic Slicing DCS；If other sequence σ can be foundmSo thatAnd order
σ=σi∪σj∪σm, calculate σjIn triggering transition tm, export DCS Dynamic Slicing result of calculations；
S44, if it is not, then cycle criterion it is all triggering transition sequences whether be final state transition sequence, if there is certain two
Data (xs, ys, zs, gs) and (xl, yl, zl, gl), s, l is integer, and x, y, z, g are transaction data so that for same state
Gs≠g1；
S45, acquisition final state transition sequence, make TDFor (xs, ys, zs, gs) and (xl, yl, zl, gl) association transition set；
S46, judge whether the corresponding behavior of final state transition sequence corresponding data of cutting into slices are consistent, obtain data consistency judgement
As a result, DCS Dynamic Slicing result of calculations are exported, DCS=DCS is otherwise exported, algorithm is terminated.
Fig. 5 and Fig. 6 are referred to, the weak section detecting step schematic diagram and Trading Model transition state of the present invention is shown as
Schematic diagram, refers to Fig. 5 and Fig. 6, S5, user behavior and data exception is detected according to uniformity result, and locking causes data different
Normal transaction system weak section, including：
S51, acquisition uniformity judged result, due toWherein x represents the amount of money, and y represents integration,
S is the price of each commodity,(1000 interchangeable 1 yuan of integration) so we can obtain data knot
Really；
S52, judge that according to uniformity judged result the corresponding behavior of final state transition sequence corresponding data of cutting into slices are consistent, can
Knowing has 4 sequences to reach state P6, respectively transition sequence σ1=tlt2t4t7, σ2=tlt2t4t6t5t7, σ3=tlt2t4t5t6t7,
σ4=tlt2t4t5t7t6, and
S53, if so, then all final state transition sequences of cycle criterion and its corresponding data slicer, find in upper table and reach
To same state P5Series；
S54, if it is not, then determining appearance exceptions area in behavior model according to the behavior corresponding final state sequence that reaches of cutting into slices
, there are two data (x in domain4, y4, z4, g4) and (x8, y8, z8, g8) same state P5 is reached, and There is g4≠g8.So obtain DCS=slice (M0,
C, P6)Y{{·lTDUnderstand its weak section as shown in Fig. 6 dashed regions.
Referring to Fig. 7, being shown as a kind of system detectio system module signal based on data slicer uniformity of the present invention
Figure, as shown in fig. 7, a kind of system detectio system 1 based on data slicer uniformity, including：Model acquisition module 11, behavior mould
Type builds module 12, slice analysis module 13, the consistent judge module 14 of data and weak section detection module 15；Model obtains mould
Block 11, for obtaining customer transaction behavior trace, and according to customer transaction behavior trace structure user's control flow model, and according to
The transaction data of user, constructs the data flow model of the user, for the customer transaction behavior trace captured, builds base
Control and the transaction system of data flow in PN machines；Behavior model builds module 12, for according to control flow model and data flow
Model integrates the behavior model for obtaining the user, builds the user's control flow model based on BPMN, and according to customer transaction data
Information, the data flow model of structuring user's obtains the behavior model of user with this, and behavior model builds module 12 and obtained with model
Module 11 is connected；Slice analysis module 13, for according to user behavior and data characteristics analytical behavior model and data model
Section, obtains dynamic behavior section and data slicer, customer transaction system of the analysis bag containing user behavior and data characteristics
Overall section, obtains data dependence information, and slice analysis module 13 builds module 12 with behavior model and is connected；Data unanimously judge
Module 14, for the uniformity for the data cut into slices according to the behavior of data dependence infomation detection, obtains consistency detection result, right respectively
Dependence and data dependence relation between the behavior of user are analyzed, and give detecting system controlling stream and data flow
Vulnerability analysis method, is analyzed by the behavior to user first, while by analyzing the dependence between user data,
By the consistency analysis to transaction data and behavior, the data slicer of uniformity is obtained, the consistent judge module 14 of data is with cutting
Piece analysis module 13 is connected；Weak section detection module 15, for detecting user behavior and data exception according to uniformity result,
Judgement causes the transaction system weak section of data exception, the fragility of the reason for obtaining causing system exception, i.e. system, the skill
Art can integrated system controlling stream and traffic flow information, and more accurately judged with this fragility to system, so as to carry
Preceding defence is ready, and weak section detection module 15 is connected with data consistency judge module 14.
Referring to Fig. 8, the model acquisition module schematic diagram of the present invention is shown as, as shown in figure 8, model acquisition module 11,
Including：Transaction data acquisition module 111, transaction data acquisition module 112, triggering transition sequence module 113, control flow model structure
Model block 114, data flow model and build module 115；Transaction data acquisition module 111, for the operation from tested examining system
The transaction data of system user is obtained in journey, as a kind of marketing tool, in recent years, " birthday gift is released in day cat store
Bag ", member gives double integration in week birthday (birthday the first six day and on the day of) purchase commodity.So-called " double " refers to, except terrible
Integrated to being done shopping with identical in the past, also enjoy and being integrated with the integration equivalent birthday of doing shopping during purchase commodity.The spy of birthday gift bag
Power is, if returning goods, and only returns shopping integration, and buyer still possesses birthday integration；Transaction trace acquisition module 112, is used for
The trading activity tracking information of system user is obtained according to transaction data, first against each customer transaction row captured
For trace, be all completion, land in the system of a closing the step of whole " wash sale, gain integration, integration by cheating cash "
Ground et al. is using 6 days cat shop of actual control, and the role of " buyer " and " seller " are played the part of in this 6 shops simultaneously.At this 6
In shop, they have made each step during different virtual goodses links, each goods links respective operations：
The activation birthday is franchise, gain integration by cheating, integration is cashed.Process substantially is that, by activating birthday privilege, buyer's account obtains the birthday
Integration；Another virtual goodses then are bought in actually other shops of control, with integration deduction payment, integration is also reformed into
Cash, is got in the account of seller et al., and transaction trace acquisition module 112 is connected with transaction data acquisition module 111；Triggering becomes
Block 113 is moved, for trading activity tracking information to be switched to trigger transition sequence, whereinP′
∪ T '=slice (Mn, σ, Qc),And F '=F |(P ', T '), wherein P ' is state set, and T ' changes for triggering
Set, MnTo identify and constraining, σ is transition sequence, and F is controlling stream set, and triggering transition sequence module 113 obtains mould with transaction
Block 112 is connected；Control flow model builds module 114, for building control according to the transaction flow of system user in monitored system
Flow model processed, control flow model builds module 114 and is connected with transaction trace acquisition module 112,
(1000 interchangeable 1 yuan of integration), g=g (z, s)=z+ ω s can be translated into Labelled Petri Net model, x represents the amount of money, y
Integration is represented, s is the price of each commodity, and ω is weights, and control flow model builds module 114 and triggering transition sequence module
113 connections；Data flow model builds module 115, for building data flow model according to transaction data, with according to trade gold
Volume, the transaction data such as integration and commodity price builds data flow model, and data flow model builds module 115 and obtained with transaction data
Modulus block 111 is connected.
Referring to Fig. 9, the slice analysis module diagram of the present invention is shown as, as shown in figure 9, slice analysis module 13,
Including：Operation information acquisition module 131, data relationship module 132, characteristic extracting module 133, dynamic behaviour section module 134,
Behavior model analysis module 135；Operation information acquisition module 131, during for occurring user's operation in tested examining system, is obtained
The transaction data of the user；Data relationship module 132, for converting transaction data for data relationship information, the number
It is the data dependence information in behavior model according to relation information, data relationship module 132 connects with operation information acquisition module 131
Connect；Characteristic extracting module 133, for obtaining behavior characteristic information from transaction data, is extracted in transaction data
The behavior characteristic informations such as dealing money, online transaction account integration, the price of tradable commodity, characteristic extracting module 133 and operation
Data obtaining module 131 is connected；Dynamic behaviour section module 134, for according to data relationship information and behavior characteristic information, leading to
The behavior model for crossing user structure carries out slice analysis, obtains dynamic behaviour section, dynamic behaviour section module and data relationship
Module is connected, and makes N=(P, T, F), is a Petri network and is made<M0, Q>, it is a N section standard；The slice that cuts into slices expires
Sufficient condition：For each sequence σ=t2Ltn,So that Mn-1(p) ＜ Mn(p), for some Qc and<
N ', M '0>, wherein presence one occurs sequence σ ' and σ ' is σ subsequence；M≤n and M 'mCover Mn|P′
(i.e. M 'm≥Mn|P′), wherein M is mark and constrains, and σ is transition sequence, and F is controlling stream set, dynamic behaviour section module 134
It is connected with characteristic extracting module 133；Behavior model analysis module 135, it is special for the data behavior in slice analysis behavior model
Levy, data dependence information is obtained, for a sequence σ=t2LtnIts Dynamic Slicing can be calculated<M0, σ, Q>It is as follows：N subnet
N=(P, T, F), for all from M0To PqSequence σ1, σ2..., σnIf,So export DCS
=slice (Mi, C, Pq), M is mark and constrains, and σ is transition sequence, and F is controlling stream set, and DCS cuts into slices for behavior, behavior mould
Type analysis module 135 is connected with dynamic behaviour section module 134.
Referring to Fig. 10, being shown as the consistent judge module schematic diagram of data of the present invention, as shown in Figure 10, data are unanimously sentenced
Disconnected module 14, including：Transition acquisition module 141, combination final state judge module 142, data slicer computing module 143, transition are followed
Ring judge module 144, final state retrieval module 145 and uniformity judge module 146；Acquisition module 141 is changed, for obtaining
All triggering transition sequences and data dependence information；Final state judge module 142 is combined, for traveling through all triggering transition sequences,
Final state transition sequence is determined whether according to behavior model and data dependence information, ifAnd only exist
One sequence σ1Reach Pq, calculate σiIn triggering transition ti, then DCS={ ti}∪slice(Mi, σi, Pq∪·ti)；Until
Find the last item sequence σhSo thatAnd make σ=σi∪σj∪σm∪L∪σh, calculate σhIn triggering become
Move th, output DCS=DCS ∪ { th}∪{·th, wherein P is state set, and t changes for triggering, and M is mark and constrains, and σ is
Transition sequence, F is controlling stream set, and i, j, m are integer；S43, if so, then calculating the final state transition sequence according to logic of propositions
Corresponding data slicer, if an other sequence σ can be foundjSo thatAnd make σ=σi∪σj, export DCS=
slice(Mi, σ, Pq) DCS=slice (Mi, σ, Pq), data consistency can be tried to achieve according to Dynamic Slicing DCS, if it can be found
His sequence σmSo thatAnd make σ=σi∪σj∪σm, calculate σjIn triggering transition tm, output DCS=DCS ∪
{tm}∪{·tm, DCS is Dynamic Slicing, and data slicer computing module 143 is connected with combining final state judge module 142；Transition are followed
Ring judge module 144, in the non-final state transition sequence of triggering transition sequence, whether all triggering transition sequences of cycle criterion
For final state transition sequence, if there is certain two data (xs, ys, zs, gs) and (xl, yl, zl, gl), s, l is integer, x, y, z, g
For transaction data so that for the g of same states≠g1, transition cycle criterion module 144 is with combining final state judge module 142
Connection；Final state retrieval module 145, for obtaining final state transition sequence, makes TDFor (xs, ys, zs, gs) and (xl, yl, zl,
gl) association transition set, final state retrieval module 145 connects with combining final state judge module 142；Uniformity judge module
146, for judging whether the corresponding data of the corresponding behavior section of final state transition sequence are consistent, obtain data consistency and judge knot
Really, output DCS=DCS ∪ { TD, algorithm is terminated, and otherwise exports DCS=DCS, and algorithm is terminated, uniformity judge module 146
It is connected with final state retrieval module 145.
Figure 11 is referred to, the weak section detection module schematic diagram of the present invention is shown as, as shown in figure 11, weak section inspection
Module 15 is surveyed, including：Uniformity result acquisition module 151, fragility judge module 152, final state sequence loops judge module
153 and abnormal area identification module 154；Uniformity result acquisition module 151, for obtaining uniformity judged result, due toWherein x represents the amount of money, and y represents integration, and s is the price of each commodity,(1000 interchangeable 1 yuan of integration) so we can obtain data result；Fragility judge module
152, for judging that the corresponding data of behavior section are consistent according to uniformity judged result, it is known that there is 4 sequences to reach state P6,
Respectively transition sequence σ1=tlt2t4t7, σ2=tlt2t4t6t5t7, σ3=tlt2t4t5t6t7, σ4=t1t2t4t5t7t6, and Fragility judge module 152 is connected with uniformity result acquisition module 151；Final state sequence is followed
Ring judge module 153, for behavior cut into slices corresponding data it is consistent when, all final state transition sequences of cycle criterion and its correspondingly
Data slicer, searching reach same state P5Series, final state sequence loops judge module 153 and fragility judge module
152 connections；Abnormal area identification module 154, for when the corresponding data of behavior section are inconsistent, being cut into slices according to behavior corresponding
Arrival final state sequence determine appearance abnormal area in behavior model, have two data (x4, y4, z4, g4) and (x8, y8, z8,
g8) reach same state P5, andThere is g4≠
g8.So obtain DCS=slice (M0, C, P6)Y{·TDIts weak section is understood as shown in Fig. 6 dashed regions, abnormal area
Identification module 154 is connected with fragility judge module 152.
In summary, the present invention provides a kind of system detecting method and system based on data slicer uniformity, the present invention
A kind of system detecting method and system based on data slicer uniformity provided, has the advantages that：Pass through user's
Controlling behavior and the inconsistent detection of data, are detected so that transaction system using microtomy to the security of transaction system
The uniformity and behavior congruence of fund are disclosure satisfy that, the robustness of transaction system on the one hand can be improved, on the other hand to handing over
Easy system can be defendd in advance.The present invention is analyzed by the behavior to user first, while by analyzing number of users
According to dependence, by the consistency analysis to transaction data and behavior, the data slicer of uniformity is obtained, so as to obtain
The fragility of the reason for causing system exception, i.e. system.The fragility that this patent studies system from Petri network microtome angle is asked
Topic, it is proposed that the transaction system detection method based on the section of fund and behavior unanimously, the matching solved in conventional art is wrong
Miss and the technical problem that rule action conflicts is flowed in wrong report, with very high commercial value and practicality.
Claims (10)
1. a kind of system detecting method based on data slicer uniformity, it is characterised in that including：
Customer transaction behavior trace is obtained, and according to customer transaction behavior trace structure user's control flow model, and according to
The transaction data of user, constructs the data flow model of the user；
The behavior model for obtaining the user is integrated according to the control flow model and the data flow model；
The section of the behavior model and the data model is analyzed according to user behavior and data characteristics, dynamic behavior is obtained
Section and data slicer, the overall section of customer transaction system of the analysis bag containing the user behavior and data characteristics, obtain number
According to Dependency Specification；
The uniformity for the data that the behavior according to the data dependence infomation detection is cut into slices, obtains consistency detection result；
User behavior and data exception are detected according to the uniformity result, locking causes the transaction system of the data exception crisp
Weak-strong test.
2. according to the method described in claim 1, it is characterised in that the acquisition customer transaction behavior trace, and according to described
Customer transaction behavior trace builds user's control flow model, and according to the transaction data of user, constructs the data of the user
Flow model, including：
The transaction data of system user is obtained from the running of tested examining system；
The trading activity tracking information of the system user is obtained according to the transaction data；
The trading activity tracking information is switched to trigger transition sequence；
Control flow model is built according to the transaction flow of system user in the monitored system；
Data flow model is built according to the transaction data.
3. method according to claim 1 or 2, it is characterised in that described that institute is analyzed according to user behavior and data characteristics
The section of behavior model and the data model is stated, dynamic behavior section and data slicer is obtained, analysis bag contains the user
The overall section of the customer transaction system of behavior and data characteristics, obtains data dependence information, including：
When user's operation occurring in tested examining system, the transaction data of the user is obtained；
The transaction data is converted for data relationship information；
Behavior characteristic information is obtained from the transaction data；
According to the data relationship information and the behavior characteristic information, the behavior model built by the user carries out section point
Analysis, obtains dynamic behaviour section；
Data behavioural characteristic in behavior model described in slice analysis, obtains data dependence information.
4. according to the method described in claim 1, it is characterised in that the behavior according to the data dependence infomation detection
The uniformity of the data of section, obtains consistency detection result, including：
Obtain all triggering transition sequences and the data dependence information；
All triggering transition sequences are traveled through, final state is determined whether according to the behavior model and the data dependence information
Transition sequence；
If so, then calculating the corresponding data slicer of the final state transition sequence according to logic of propositions；
If it is not, then whether all triggering transition sequences of cycle criterion are final state transition sequence；
Obtain the final state transition sequence；
Judge whether the corresponding data of the corresponding behavior section of the final state transition sequence are consistent, obtain data consistency and judge knot
Really.
5. according to the method described in claim 1, it is characterised in that it is described according to the uniformity result detect user behavior and
Data exception, locking causes the transaction system weak section of the data exception, including：
Obtain the uniformity judged result；
Judge that the corresponding data of the corresponding behavior section of the final state transition sequence are consistent according to the uniformity judged result；
If so, then all final state transition sequences of cycle criterion and its corresponding data slicer；
If it is not, then determining the appearance exceptions area in the behavior model according to the corresponding arrival final state sequence of behavior section
Domain.
6. a kind of system detectio system based on data slicer uniformity, it is characterised in that including：Model acquisition module, behavior
Model construction module, slice analysis module, the consistent judge module of data and weak section detection module；
The model acquisition module, builds for obtaining customer transaction behavior trace, and according to the customer transaction behavior trace
User's control flow model, and according to the transaction data of user, construct the data flow model of the user；
The behavior model builds module, and the user is obtained for being integrated according to the control flow model and the data flow model
Behavior model；
The slice analysis module, for analyzing the behavior model and the data model according to user behavior and data characteristics
Section, obtain dynamic behavior section and data slicer, customer transaction of the analysis bag containing the user behavior and data characteristics
The overall section of system；
The consistent judge module of the data, the data cut into slices for the behavior according to the data dependence infomation detection it is consistent
Property, obtain consistency detection result；
The weak section detection module, for detecting user behavior and data exception according to the uniformity result, judges to draw
Play the transaction system weak section of the data exception.
7. system according to claim 6, it is characterised in that the model acquisition module, including：Transaction data obtains mould
Block, transaction trace acquisition module, triggering transition sequence module, control flow model build module, data flow model and build module；
The transaction data acquisition module, for from the running of tested examining system obtain system user number of deals it is believed that
Breath；
The transaction trace acquisition module, the trading activity track for obtaining the system user according to the transaction data
Mark information；
The triggering transition sequence module, for switching to the trading activity tracking information to trigger transition sequence；
The control flow model builds module, is controlled for being built according to the transaction flow of system user in the monitored system
Flow model；
The data flow model builds module, for building data flow model according to the transaction data.
8. the system according to claim 6 or 7, it is characterised in that the slice analysis module, including：Operation information is obtained
Modulus block, data relationship module, characteristic extracting module, dynamic behaviour section module, behavior model analysis module；
The operation information acquisition module, during for occurring user's operation in tested examining system, obtains the friendship of the user
Easy data message；
The data relationship module, for converting the transaction data for data relationship information；
The characteristic extracting module, for obtaining behavior characteristic information from the transaction data；
The dynamic behaviour section module, for according to the data relationship information and the behavior characteristic information, passing through the use
The behavior model that family is built carries out slice analysis, obtains dynamic behaviour section；
The behavior model analysis module, for the data behavioural characteristic in behavior model described in slice analysis, obtain data according to
Rely information.
9. system according to claim 7, it is characterised in that the consistent judge module of the data, including：Transition obtain mould
Block, combination final state judge module, data slicer computing module, transition cycle criterion module, final state retrieval module with it is consistent
Property judge module；
The transition acquisition module, for obtaining all triggering transition sequences and the data dependence information；
The combination final state judge module, for traveling through all triggering transition sequences, according to the behavior model and described
Data dependence information determines whether final state transition sequence；
The data slicer computing module, for when the triggering transition sequence is final state transition sequence, according to logic of propositions
Calculate the corresponding data slicer of the final state transition sequence；
The transition cycle criterion module, in the non-final state transition sequence of the triggering transition sequence, cycle criterion to be owned
Trigger whether transition sequence is final state transition sequence；
The final state retrieval module, for obtaining the final state transition sequence；
The uniformity judge module, for judge the corresponding behavior of the final state transition sequence cut into slices corresponding data whether one
Cause, obtain data consistency judged result.
10. system according to claim 7, it is characterised in that the weak section detection module, including：Uniformity knot
Fruit acquisition module, fragility judge module, final state sequence loops judge module and abnormal area identification module；
The uniformity result acquisition module, for obtaining the uniformity judged result；
The fragility judge module, for judging the corresponding data one of the behavior section according to the uniformity judged result
Cause；
The final state sequence loops judge module, for when the corresponding data of behavior section are consistent, cycle criterion to be owned
The final state transition sequence and its corresponding data slicer；
The abnormal area identification module, for when the corresponding data of behavior section are inconsistent, being cut according to the behavior
The corresponding arrival final state sequence of piece determines the appearance abnormal area in the behavior model.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108089136A (en) * | 2017-12-28 | 2018-05-29 | 上海神力科技有限公司 | A kind of fuel cell pile polarization curve test data auto slice method |
| CN109635567A (en) * | 2019-01-29 | 2019-04-16 | 腾讯科技（深圳）有限公司 | For the method for calibration of applications client, device and server platform |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679484A (en) * | 2014-01-09 | 2014-03-26 | 安徽理工大学 | Novel method for analyzing E-commerce consistency based on behavior Petri network |
| CN105005901A (en) * | 2015-07-09 | 2015-10-28 | 厦门快商通信息技术有限公司 | Financial field oriented transaction fraud detection system and method |
| CN105069554A (en) * | 2015-07-22 | 2015-11-18 | 安徽理工大学 | Novel method for analyzing inconsistent behaviors of data-restricted business process models |
| CN106169111A (en) * | 2016-04-21 | 2016-11-30 | 铜仁学院 | A kind of configurable business process analysis method based on the constraint of data stream |
-
2017
- 2017-05-03 CN CN201710306324.1A patent/CN107220548B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679484A (en) * | 2014-01-09 | 2014-03-26 | 安徽理工大学 | Novel method for analyzing E-commerce consistency based on behavior Petri network |
| CN105005901A (en) * | 2015-07-09 | 2015-10-28 | 厦门快商通信息技术有限公司 | Financial field oriented transaction fraud detection system and method |
| CN105069554A (en) * | 2015-07-22 | 2015-11-18 | 安徽理工大学 | Novel method for analyzing inconsistent behaviors of data-restricted business process models |
| CN106169111A (en) * | 2016-04-21 | 2016-11-30 | 铜仁学院 | A kind of configurable business process analysis method based on the constraint of data stream |
Non-Patent Citations (1)
| Title |
|---|
| 刘璐: "基于Petri网的业务流程模型一致性分析", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108089136A (en) * | 2017-12-28 | 2018-05-29 | 上海神力科技有限公司 | A kind of fuel cell pile polarization curve test data auto slice method |
| CN108089136B (en) * | 2017-12-28 | 2020-02-11 | 上海神力科技有限公司 | Automatic slicing method for fuel cell stack polarization curve test data |
| CN109635567A (en) * | 2019-01-29 | 2019-04-16 | 腾讯科技（深圳）有限公司 | For the method for calibration of applications client, device and server platform |
| CN109635567B (en) * | 2019-01-29 | 2022-12-16 | 腾讯科技（深圳）有限公司 | Verification method and device for application client and server platform |
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