AU2017100012A4 - Method for measuring user behavior consistency degree based on complex correspondence system - Google Patents
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Abstract
Abstract of the Disclosure A method for measuring user behavior consistency degree based on a complex correspondence system, which is applied to internet payment platform security. An entire solution is divided into three stages: at a first stage, analyzing complex correspondence relation characteristics according to an existing user behavior model; at a second stage, establishing a behavior profile according to user behavior characteristics and establishing user behavior relation matrixes; and at a third stage, completing user behavior matrix decomposition according to the complex correspondence characteristics of a user, calculating a user behavior consistency degree and detecting a degree of consistency between user behaviors and expected behaviors. The internal behavior relation of the user is more elaborately analyzed, the user behavior relation profile is established, the complex correspondence relations are distinguished and classified, and user behavior consistency measurement and analysis architecture based on the complex correspondence relations are given. The complex correspondence relations are effectively distinguished and calculated, the problem of measurement of a behavior consistency of a complex correspondence model pair is solved, and the operation time is greatly shortened.
Description
METHOD FOR MEASURING USER BEHAVIOR CONSISTENCY DEGREE BASED ON COMPLEX CORRESPONDENCE SYSTEM
The present application is a divisional application from International Patent Application No. PCT/CN2014/095859 filed on 31 December 2014, the entire disclosure of which is incorporated herein by reference.
Background of the Present Invention
Field of Invention
The present invention relates to a measurement for user behavior consistency degree, which can be applied to internet payment platform security.
Description of Related Arts
With the rapid development of computers, the application of online payment platforms increasingly becomes wider, and the requirements on detection technologies of the behavior consistency in payment processes of users also increasingly become stricter.
Since system designers and modelers hold different points of view on the same real world phenomenon, different models are established consequently. Consistency of models is related to matching semantics of models elements under model matching situations. As a result, complex correspondence situations exist self-evidently. According to statistics, for correspondence existing in process models, more than 40% is complex correspondence and more than 7% is cross repetitive correspondence. How to perform consistency analysis to user behaviors and expected behaviors in electronic transaction processes obviously has a critical significance to models existing complex systems.
In the past, some researches were carried out to consistency between two models (i.e., a user behavior measurement model and an expected model), and measurement methods such as trace matching, mutual simulation and behavior profiling were put forward (see notes [1-5] below). However, these methods cannot effectively distinguish situations of complex correspondence between behaviors in the aspect of complex correspondence, such that the calculation accuracy is greatly discounted.
The following indexes are provided, and open literatures corresponding to the indexes are close or related arts of the technical solution of the present invention and are viewed as part of the description of the present invention. Therefore, for technical terms which are involved in the technical solution of the present invention and prior arts on which the implementation of the technical solution depends, a reference can be made to the following information: [1] Matthias Weidlich, Jan Mendling, Mathias Weske. Efficient consistency measurement based on behavioral profiles of process models [J], IEEE Transactions on Software Engineering, 2001, 37(3): 410-129.
[2] MatthiasWeidlic, Behavioral profiles -a relational approach tobehavior consistency [DB/OL], Institutional Repository of the University of
Potsdam:URLhttp://opus.kobv.de/ubp/volltexte/2011/5559/URN urn:nbn:de:kobv:517-opus-55590, 2011.
[3] Sergey Smirnov, Matthias Weidlich, Jan Mendling. Business Process Model Abstraction Based on Behavioral Profiles [C]. Heidelberg: SpringerVerlag, 2010: 1-16.
[4] MatthiasWeidlich, Mathias Weske, Jan Mendling. Change Propagation in Process Models Using Behavioral Profiles[C], Washington: IEEE Computer Society Washington, 2009: 33-40.
[5] Matthias Weidlich, Jan Mendling. Perceived consistency between process models[J], Information Systems, 2012, 37(2): 80-98.
Summary of the Present Invention
The purpose of the present invention is to overcome the defects of the prior art, so as to measure behavior consistency between a user behavior model and an expected model, perform specific classified analysis to complex correspondence behavior relations and determine behavior correspondence characteristics of all complex classes; and solve the problem of measurement of behavior consistency containing cross repetitive correspondence, calculate behavior consistency of models by using knowledge related to matrixes and measure a compliance degree of behavior consistency containing complex correspondence relations.
For this purpose, the following technical solution is adopted: A method for measuring user behavior consistency degree based on a complex correspondence system is characterized in that an entire solution is divided into three stages: a first stage comprising the following specific implementation steps: step 1-1: subdividing cross order relations based on an existing workflow net, and refining behavior profile relations; step 1-2: analyzing complex correspondence relations, classifying the complex correspondence relations and determining behavior characteristics of each class; and step 1-3: simultaneously analyzing transitive dependency relations between user activities according to indirect relations between users, wherein steps 1-1, 1-2 and 1-3 are performed in parallel; a second stage comprising the following specific implementation steps: step 2-1: determining correlations between five classes of correspondence relations according to the classification of the complex correspondence relations completed in step 1-2 and the behavior characteristics of each class; step 2-2: establishing user extended behavior profile relations according to the behavior profile relations refined in step 1-1; step 2-3: converting user behavior relations into matrix elements based on step 2-2 in combination with step 1-3 according to a formula
(i,j=l,2,.. ,,n) (wherein ay denotes elements in behavior relation matrix); and step 2-4: establishing a user behavior relation matrix graph based on steps 2-2 and 2-3, wherein an establishment step thereof is as follow from matrix MD i —*h4I) 2—>MI) j—>MI) 4... —>MI)n—>MD):
a third stage comprising the following specific implementation steps: step 3-1: decomposing the user behavior relation matrixes according to the five user complex correspondence relation classes determined in step 2-1 and the behavior relation matrix graph established in step 2-4; and step 3-2: calculating behavior consistency between a user model and an expected model according to correspondence relations between an actual model and the expected model of a user, calculation formula:
wherein consistent behavior relations show consistent portions of user activities, area of behavior matrixes is used for depicting entire consistent behavior relations thereof, a higher consistency value represents that user behaviors and expected behaviors are more consistent, a lower consistency value represents that the user behavior and the expected behaviors are more inconsistent, and when consistency is particularly low, the user behaviors are suspected as illegal behaviors.
Brief Description of the Drawings
Fig. 1 is a system architecture diagram.
Fig. 2 is a business process Petri net diagram.
Fig. 3 is a behavior relation graph of Fig. 2.
Fig. 4 is a decomposition graph of Fig. 3.
Fig. 5 is a flowchart of algorithm 1.
Fig. 6 is a flowchart of algorithm 2.
Detailed Description of the Preferred Embodiments
More delicate analysis is performed on internal behavior relations of a user, profiles of user behavior relations are established, complex correspondence relations are distinguished and classified, and user behavior consistency measurement and analysis architecture based on the complex correspondence relations is given, as shown in Fig. 1. This architecture can effectively distinguish the complex correspondence relations and accordingly make a more accurate judgment to behavior correspondence relations. The complex correspondence relations are effectively distinguished and calculated, the problem of behavior consistency measurement of complex correspondence model pairs is solved and the operation time is greatly shortened.
As shown in Fig. 1 which illustrates a system structural diagram of a method for measuring a user behavior consistency degree, an entire solution is divided into three stages: at a first stage, analyzing complex correspondence relation characteristics according to a traditional user behavior model, at a second stage, establishing a behavior profile according to user behavior characteristics and establishing user behavior relation matrixes, and at a third stage, completing user behavior matrix decomposition according to the complex correspondence characteristics of a user, calculating a user behavior consistency degree and detecting a degree of consistency between user behaviors and expected behaviors.
The first stage comprises the following specific implementation steps: step 1-1: subdividing cross order relations based on an existing workflow net, and refining behavior profile relations; step 1-2: analyzing complex correspondence relations, classifying the complex correspondence relations and determining behavior characteristics of each class; and step 1-3: simultaneously analyzing transitive dependency relations between user activities according to indirect relations between users,
Wherein, steps 1-1, 1-2 and 1-3 are performed in parallel.
The second stage comprises the following specific implementation steps: step 2-1: determining correlations between five classes of correspondence relations according to the classification of the complex correspondence relations completed in step 1-2 and the behavior characteristics of each class; step 2-2: establishing user extended behavior profile relations according to the behavior profile relations refined in step 1-1; step 2-3: converting user behavior relations into matrix elements based on step 2-2 in combination with step 1-3 according to a formula
(/,7=1,2,...,n) (wherein ay denotes elements in behavior relation matrix); and step 2-4: establishing a user behavior relation matrix graph based on steps 2-2 and 2-3. wherein, an establishment step thereof is as follow (from matrix MD1^MD2-^MD3-^MD4... —»MD„—»MD):
The third stage comprises the following specific implementation steps: step 3-1: decomposing user behavior relation matrixes according to the five user complex correspondence relation classes determined in step 2-1 and the behavior relation matrix graph established in step 2-4 (for details, see algorithm 1); and step 3-2: calculating behavior consistency between a user model and an expected model according to correspondence relations between an actual model and the expected model of a user (for details, see algorithm 2), calculation formula:
wherein consistent behavior relations show consistent portions of user activities, area of behavior matrixes is used by us for depicting entire consistent behavior relations thereof, a higher consistency value represents that user behaviors and expected behaviors are more consistent, a lower consistency value represents that the user behavior and the expected behaviors are more inconsistent, and when consistency is particularly low, the user behaviors are suspected by us as illegal behaviors.
Algorithm 1: a solution algorithm of elements in behavior relation matrix graph (for specific processes, see Fig. 5) input: two workflow nets Ni=(Pi,Tj;Fj) and N2=(P2i,T2;Fi), wherein they have transition sets of correspondence relations
?
behavior matrixes MDAo and MI)no for ordering; output: elements a,j(ij= 1,2,...,n) and bij (i,j= 1,2,...,m) in behavior relation matrix graphs MDa and MDB; (1) firstly determining elements au (/=1,2,,..,n) of diagonals in MDa, sequentially judging whether a, (/=1,2,...,n) is in a ring structure or not, and if a, is not in the ring structure, outputting a„=2 and executing step (2); or else, outputting a,,=0 and executing step (2); (2) then determining values of au+1 and ai+u (/=1,2,...,n-l), in the net N], sequentially calculating behavior relations between a, and a,, /, then converting the behavior relations into an integer p, outputting
, and executing step (3); (3) then determining values of a;,;+2 and ai+2,i (/=1,2,...,n-2); if
outputting
or else, if
outputting
or else, if au+i=a,< ι,,ι2D, judging behavior relations between a, and ai+2 and converting the behavior relations into a relation value q, outputting au+2=ai+2,,=q, and executing step (4); (4) similarly, determining au+h and
outputting
and ending the algorithm till the last element ai„.
Similarly, we calculate elements
in MDB according to the algorithm 1 to obtain a matrix MDn.
Algorithm 2: a solution algorithm of consistency degree (for specific processes, see Fig. 6) input: two workflow nets
wherein relation matrixes MDao and MDB0 thereof are solved through the algorithm 1;
output: consistency degree BP (1) firstly and respectively dividing MDA0 and MDBo into p and q corresponding sets according to correspondence relations of the transition sets in MDao and MDBo, sequentially marking
and executing step (2); (2) firstly taking and marking first m order square matrixes in MDao as a module 1 according to a first set {a}, a2, corresponding toMDB0, inMDAo, and executing step (3); (3) taking and marking an mx(l-m) order matrix consisting of 1—Km) rows and (m+1)—Kl) columns in MDAo and a transposed matrix thereof as a module 2 according to a second set {am+1, am+2, ···,«;}, corresponding toMDBo, in MDao, and executing step (4); (4) following the previous step till a pth set {as+1, ...,an), corresponding to MDB0, in MDAo, taking and marking an mX(n-s) order matrix consisting of 1—Km) rows and (s+1)—>(n) columns in MDAo and a transposed matrix thereof as a module p, and executing step (5); (5) taking and marking an (1-m) order matrix consisting of (m+1)—>(1) rows and(m+l)—>(1) columns in MDAo as a module p+1 according to a second set {am+1, am+2, ...,a}}, corresponding to MDBo, in MDao, and executing step (6); (6) following step (4), marking a (l-m)x(n-s) order matrix consisting of (m+1 )—Kl) rows and (s+1 )—Kn) columns in MDao and a transposed matrix thereof as a module p+2, and executing step (7); (7) performing operation in this way till a pth set {as+1, ...,an}, corresponding to MDB0, in MDao, taking and marking a (n-s) order matrix consisting of s+1—>n rows and s+1—>n columns as a module
and executing step (8); (8) if p=q, similarly also decomposing MDB0 into
corresponding modules, marking the modules as module 1, 2,...
, and executing step (10); or else, if pKq, also decomposing non-repetitive correspondence relations in MDBo into
corresponding modules, and executing step (9); (9) locking repetitive corresponding transition sets, sequentially marking areas consisting of the repetitive corresponding sets as module
and executing step (10); and (10) sequentially checking matrix elements in module 1, 2,....
in MDao, finding out ah a, and different elements bh bj in the same module of MDbo, if p=q, outputting a consistency degree BP, and ending the algorithm, and if p^q, locking module lc, 2c,...,(q-p)c, outputting a consistency degree BP, and ending the algorithm.
An example of Fig. 2 is given below.
According to the algorithm 1, behavior relation matrix graphs MDa, MDb, MDC and MDain Fig. 2(a), (b), (c) and (d) (as shown in Fig. 3) are respectively obtained, and then decomposition is respectively performed according to steps (1)-(9) of the algorithm 2, by taking MDa and MDb as an example, as shown in Fig. 4. According to step (10) of the algorithm 2, a consistency degree between (a) and (b) in Fig. 2 can be obtained as follow:
similarly a consistency degree between (c) and (d) in Fig. 2 can be obtained as follow:
and in (c) and (d) in Fig. 2, * ~ {&r,*43lfA32^
ind thus a profile consistency degree between (c) and (d) in Fig. 2 is as follow:
A consistency degree between a user behavior (a) and a user behavior (b) as shown in Fig. 2 reaches 75%, a consistency degree between a user behavior (b) and a user behavior (c) as shown in Fig. 2 reaches 80%, a consistency degree between a user behavior (c) and a user behavior (d) as shown in Fig. 2 reaches 81% and all consistency degrees are comparatively high, indicating that the user behaviors are consistent with the expected behaviors, such that we judge that the user behaviors are legal behaviors.
Innovative Points of the Invention 1. User behavior mode consistency is quantified by using a behavior profile technology. 2. User complex behavior relations are classified and behavior characteristics and natures of each complex class are determined. 3. A behavior matrix method is put forward, behavior relations between model pairs are converted into elements of behavior relation matrixes and calculation time is shortened. 4. Cross repetitive correspondence situations are distinguished, accuracy is improved and the problem of measurement of behavior consistency between cross repetitive models is solved.
Claims (3)
1. A method for measuring a user behavior consistency degree based on a complex correspondence system, characterized in that an entire solution is divided into three stages: a first stage comprising the following specific implementation steps: step 1-1: subdividing cross order relations based on an existing workflow net, and refining behavior profile relations; step 1-2: analyzing complex correspondence relations, classifying the complex correspondence relations and determining behavior characteristics of each class; and step 1-3: simultaneously analyzing transitive dependency relations between user activities according to indirect relations between users; wherein the steps 1-1, 1-2 and 1-3 are performed in parallel; a second stage comprising the following specific implementation steps: step 2-1: determining correlations between five classes of correspondence relations according to the classification of the complex correspondence relations completed in step 1-2 and the behavior characteristics of each class; step 2-2: establishing user extended behavior profile relations according to the behavior profile relations refined in step 1-1; step 2-3: converting user behavior relations into matrix elements based on the step 2-2 in combination with the step 1-3 according to a formula
(/j=l,2,...,n) (wherein % denotes elements in behavior relation matrix); and step 2-4: establishing a user behavior relation matrix graph based on the steps 2-2 and 2-3, wherein an establishment step thereof is as follow (from matrix MD1^MD2-^MD3-^MD4... —*ΜΙ)η—*ΜΙ)):
a third stage comprising the following specific implementation steps: step 3-1: decomposing user behavior relation matrixes according to the five user complex correspondence relation classes determined in the step 2-1 and the behavior relation matrix graph established in the step 2-4; and step 3-2: calculating behavior consistency between a user model and an expected model according to correspondence relations between an actual model and the expected model of a user, calculation formula:
wherein consistent behavior relations show consistent portions of user activities, area of behavior matrixes is used for depicting entire consistent behavior relations thereof, a higher consistency value represents that user behaviors and expected behaviors are more consistent, a lower consistency value represents that the user behavior and the expected behaviors are more inconsistent, and when consistency is particularly low, the user behaviors are suspected as illegal behaviors.
2. The method for measuring a user behavior consistency degree based on a complex correspondence system according to claim 1, characterized in that, in the step 3-1 of decomposing the user behavior relation matrixes, a solution algorithm of elements in the behavior relation matrix graph thereof is as follow: input: two workflow nets
wherein they have transition sets of correspondence relations
,
,
(r = 1/1···.,®) ?
, behavior matrixes MDao and MDBo for ordering; output: elements a,, (/,/=1,2,,,,,n) and b,j (/,/= 1,2,,,,,m) in behavior relation matrix graphsMDa and MDb; (1) firstly determining elements //„ (/=1,2,...,n) of diagonals in MDa, sequentially judging whether //, (/=1,2,...,n) is in a ring structure or not, and if //, is not in the ring structure, outputting //,,=2 and executing step (2); or else, outputting //,,=0 and executing step (2); (2) then determining values of au+1 and a,,,,, (/=1,2,...,n-l), in the net Nh sequentially calculating behavior relations between a, and at, /, then converting the behavior relations into an integer p, outputting aIJ+1=a,, / ;=p, and executing step (3); (3) then determining values of au+2 and a, < 2., (/=1,2,. . .,n-2); if a,,t+ιφα,+ιi+2, outputting 0,,i+2=ai+2,i=rmn{ah]+1,ai+u+2}\, or else, if 0,,,+7=0,+7,,+2= 1, outputting au+2=ai+2:l=1; or else, if «/,/+/=0,+7,,+27^1, judging behavior relations between a, and ai+2 and converting the behavior relations into a relation value q, outputting 0,,,+2=0,+2,,=q, and executing step (4); (4) similarly, determining au+h and 0,+/,,/(/=1,2,...,n-h) (//=3,...,n-1), outputting 0,·,,·+/,=0,+/,.,, and ending the algorithm till the last element aln; similarly, calculating elements //,,(/,/= 1,2,...,m) inMDB according to the solution algorithm of the elements in the behavior relation matrix graph to obtain a matrix K1I)B.
3. The method for measuring a user behavior consistency degree based on a complex correspondence system according to claim 1, characterized in that, in the step 3-2 of calculating the behavior consistency between the user model and the expected model, a solution algorithm of a consistency degree thereof is as follow: input: two workflow nets N}=(Pi,Ti;F1) and N2=(P21,T2;F2), wherein relation matrixes ΜΙ)Λ0 and MDbo thereof are solved through the solution algorithm of the elements in the behavior relation matrix graph in the step 3-1; output: a consistency degree BP (1) firstly and respectively dividing MDA0 and MDBo into p and q corresponding sets according to correspondence relations of transition sets in MDao and MDB0, sequentially marking MDao as {aB 02, ...,am},{am+1, //„, .;·. ...,a]}...{as+1,...,an}, and executing step (2); (2) firstly taking and marking first m order square matrixes in MDao as a module 1 according to a first set {ah a2, ...//,„}, corresponding to MDB0, in MDao, and executing step (3); (3) taking and marking an mx(l-m) order matrix consisting of 1—Km) rows and (m+1)->(1) columns in MDA0 and a transposed matrix thereof as a module 2 according to a second set{am+1, am+2, .··,«;}, corresponding to MI)no, in MDA0, and executing step (4); (4) following the previous step till a pth set {as+/,...,a„}, corresponding to MDB0, in MDAo, taking and marking an mx(n-s) order matrix consisting of 1—>(m) rows and (s+1)—>(n) columns in MDA0 and a transposed matrix thereof as a module p, and executing step (5); (5) taking and marking a (1-m) order matrix consisting of (m+1 )—*(!) rows and (m+1 )—*(!) columns in MDao and a transposed matrix thereof as a module p+1 according to a second set {am+i, am+2,...///!, corresponding toMDB0, inMDAo, and executing step (6); (6) following step (4), marking a (l-m)x(n-s) order matrix consisting of (m+1)—>-(1) rows and (s+1)—^(n) columns in MDA0 and a transposed matrix thereof as a module p+2, and executing step (7) ; (7) performing operation in this way till a pth set {as+1, ...,an}, corresponding to MDB0, in MDAo, taking and marking a (n-s) order matrix consisting of s+1—mi rows and s+1—m columnsas a module
and executing step (8); (8) if p=q, similarly also decomposing MDB0 into
corresponding modules, marking the modules as modulel, 2,..
and executing step (10);or else, if p^q, also decomposing non-repetitive correspondence relations in MDB0 into
corresponding modules, and executing step (9); (9) locking repetitive corresponding transition sets, sequentially marking areas consisting of the repetitive corresponding sets as module
, and executing step (10); and (10) sequentially checking matrix elements in module 1, 2,...
in MDAo, finding out a,, a, and different elements bt, bj in the same module of MI)B0, if p=q, outputting a consistency degree BP, and ending the algorithm, and if p^q, locking module lc, 2C,...,(q-p)c, outputting a consistency degree BP, and ending the algorithm.
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| CN201410327709.2A CN104133808B (en) | 2014-07-10 | 2014-07-10 | User behavior uniformity degree measuring method based on complicated correspondence system |
| PCT/CN2014/095859 WO2016004744A1 (en) | 2014-07-10 | 2014-12-31 | Method for measuring user behavior consistency based on complex correspondence system |
| AU2017100012A AU2017100012A4 (en) | 2014-07-10 | 2017-01-05 | Method for measuring user behavior consistency degree based on complex correspondence system |
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