WO2016004744A1 - 基于复杂对应系统的用户行为一致性度测量方法 - Google Patents
基于复杂对应系统的用户行为一致性度测量方法 Download PDFInfo
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- WO2016004744A1 WO2016004744A1 PCT/CN2014/095859 CN2014095859W WO2016004744A1 WO 2016004744 A1 WO2016004744 A1 WO 2016004744A1 CN 2014095859 W CN2014095859 W CN 2014095859W WO 2016004744 A1 WO2016004744 A1 WO 2016004744A1
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- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
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Definitions
- the invention relates to user behavior consistency measurement and can be applied to internet payment platform security.
- the object of the present invention is to overcome the deficiencies of the prior art, and to measure the behavior consistency between the user behavior model and the expected model, perform specific classification analysis on the complex corresponding behavior relationship, determine the behavior corresponding features of each complex class, and solve the problem corresponding to the crossover repetition.
- the behavioral consistency problem using the relevant knowledge of the matrix, calculates the behavioral consistency of the model, and measures the behavioral consistency compliance with complex correspondence.
- a method for measuring user behavior consistency based on a complex corresponding system characterized in that the whole scheme is divided into three phases:
- Step 1-1 on the basis of the existing workflow network, subdivide the cross-order relationship and refine the behavioral contour relationship;
- Step 1-2 analyzing complex correspondences, classifying complex correspondences, and determining behavior characteristics of each class;
- Step 1-3 at the same time, analyzing the transfer dependencies between user activities according to the indirect relationship between users;
- Step 2-1 determining the correlation between the five types of correspondences according to the classification of the complex correspondences completed in step 1-2 and the behavior characteristics of each class;
- Step 2-2 establishing a behavior profile relationship of the user extension according to the behavior profile relationship refined in step 1-1;
- Step 2-4 based on steps 2-2 and 2-3, construct a user behavior relationship matrix diagram
- Step 3-1 according to the five types of user complex correspondence classes determined in step 2-1 and the behavior relationship matrix diagram established in step 2-4, the user behavior relationship matrix is decomposed;
- Step 3-2 calculating the behavior consistency between the user model and the expected model according to the correspondence between the actual model of the user and the expected model.
- the consistent behavior relationship shows the consistent part of the user activity, and the whole behavioral relationship is characterized by the area of the behavior matrix.
- Figure 3 is the behavior diagram of Figure 2
- Figure 4 is an exploded view of Figure 3
- Figure 5 is a flow chart of Algorithm 1
- Figure 6 is an algorithm 2 flow chart
- the specific implementation method analyzes the internal behavior relationship of the user in detail, establishes the outline of the user behavior relationship, and distinguishes and classifies the complex correspondence relationship, and gives the user line based on the complex correspondence relationship.
- the framework can effectively distinguish complex correspondences and make more accurate decisions on behavioral correspondences. Effectively distinguish and calculate complex correspondences, solve the behavior consistency measurement problem of complex corresponding model pairs, and greatly shorten the computing time.
- the system structure diagram of the user behavior consistency measurement method is shown in Figure 1.
- the whole scheme is divided into three phases: the first phase analyzes the complex correspondence characteristics according to the existing user behavior model, the second phase establishes the behavior contour according to the user behavior characteristics, constructs the user behavior relationship matrix, and the third stage is completed according to the user complex correspondence characteristics.
- the user behavior matrix is decomposed, the user behavior consistency is calculated, and the degree of consistency between the user behavior and the expected behavior is detected.
- Step 1-1 on the basis of the existing workflow network, subdivide the cross-order relationship and refine the behavior contour relationship.
- Step 1-2 Analyze complex correspondences, classify complex correspondences, and determine behavior characteristics of each class.
- Steps 1-3 while analyzing the transfer dependencies between user activities based on the indirect relationship between users.
- steps 1-1, 1-2 and 1-3 above are performed in parallel.
- step 2-1 according to the classification of the complex correspondences completed in step 1-2 and the behavior characteristics of each class, the correlation between the five types of correspondences is determined.
- step 2-2 the behavior profile relationship of the user extension is established according to the behavior profile relationship refined in step 1-1.
- Step 2-4 based on steps 2-2 and 2-3, construct a user behavior relationship matrix diagram.
- Step 3-1 according to the five types of user complex correspondence classes determined in step 2-1 and the behavior relationship matrix diagram established in step 2-4, the user behavior relationship matrix is decomposed (see Algorithm 1 for details).
- Step 3-2 Calculate the behavior consistency between the user model and the expected model according to the correspondence between the actual model of the user and the expected model (see Algorithm 2 for details).
- the consistent behavioral relationship shows the consistent part of the user's activity.
- step (4) MD A0 in accordance with a second MD B0 corresponding set ⁇ a m + 1, a m + 2, ... a l ⁇ , taking the first MD A0 1 ⁇ (m) rows and (m + 1) ⁇ (l)
- the m ⁇ (lm) order matrix composed of columns and its transposed matrix are denoted as module 2, and step (4) is performed.
- step (5) the previous step modeled until MD A0 in the p-th and MD B0 corresponding to the set ⁇ a s + 1, ... a n ⁇ , taking MD A0 first 1 ⁇ (m) rows and (s + 1) ⁇
- the m ⁇ (ns) order matrix composed of (n) columns and its transposed matrix are denoted as module p, and step (5) is performed.
- MD A0 in accordance with a second set of MD B0 corresponding to ⁇ a m + 1, a m + 2, ... a l ⁇ , taking the first MD A0 (m + 1) ⁇ (l ) rows and (m
- the (lm) order matrix composed of +1) ⁇ (l) columns is recorded as module p+1, and step (6) is performed.
- step (4) the (lm) ⁇ (ns) order matrix composed of (m+1) ⁇ (l) rows and (s+1) ⁇ (n) columns in MD A0 and its transposed matrix Record as module p+2 and perform step (7).
- step (9) is performed.
- the behavior relationship matrix diagrams MD a , MD b , MD c , MD d (shown in Figure 3) of Figures 2(a), (b), (c), and (d) are respectively obtained, and then according to the algorithm.
- Steps (1)-(9) of 2 are respectively decomposed, taking MD a and MD b as an example, as shown in FIG. 4 .
- the step (10) of the algorithm 2 the degree of consistency of (a) and (b) in Fig.
- the behavior of the user (a) shown in FIG. 2 is 75% consistent with the behavior of (b), and the behavior of the user (b) shown in FIG. 2 is 80% consistent with the behavior of (c), as shown in FIG.
- the behavior of user (c) is approximately 81% consistent with the behavior of (d), which is high, indicating that the user behavior is consistent with the expected behavior, and we determine that the user behavior is legal.
Abstract
Description
Claims (3)
- 一种基于复杂对应系统的用户行为一致性度测量方法,其特征在于,整个方案分为三个阶段:第一阶段具体实施步骤:步骤1-1,在已有工作流网的基础上,对交叉序关系进行细分,细化行为轮廓关系;步骤1-2,分析复杂对应关系,将复杂对应关系分类,确定各个类的行为特征;步骤1-3,同时根据用户之间的间接关系分析用户活动间的传递依赖关系;以上步骤1-1、1-2和1-3是并列进行着;第二阶段具体实施步骤:步骤2-1,根据步骤1-2完成的复杂对应关系的分类及其每个类的行为特征,确定五类对应关系之间的相关性;步骤2-2,根据步骤1-1细化的行为轮廓关系,建立用户扩展的行为轮廓关系;步骤2-4,在步骤2-2和2-3的基础上,构建用户行为关系矩阵图;其构造步骤如下所示(从矩阵MD1→MD2→MD3→MD4…→MDn→MD):第三阶段具体实施步骤:步骤3-1,根据步骤2-1确定的五类用户复杂对应类以及步骤2-4建立的行为关系矩阵图,将用户行为关系矩阵进行分解;步骤3-2,根据用户实际模型与预期模型的对应关系,计算用户模型与预期模型的行为一致性,其中,一致的行为关系表现出用户活动的一致的部分,用行为矩阵的面积来刻画其整个一致的行为关系,一致度值越高代表该用户行为与预期行为越一致,一致度值越低代表该用户行为与预期行为越不一致,当一致度特别低时,怀疑该用户行为为非法行为。
- 如权利要求1所述的基于复杂对应系统的用户行为一致性度测量方法,其特征在于,步骤3-1中,所述将用户行为关系矩阵进行分解,其行为关系矩阵图中元素的求解算法为:输入:两个工作流网N1=(P1,T1;F1)和N2=(P2,T2;F2),其中他们中有着对应关系的 变迁集A={a1,a2,…,an}、B={b1,b2,…,bm}、(i=1,2,…,n)、进行排序的行为关系矩阵MDA0和MDB0;输出:行为关系矩阵图MDA、MDB中的元素aij(i,j=1,2,…,n)、bij(i,j=1,2,…,m);(1)先确定MDA中对角线的元素aii(i=1,2,…,n),依次判断ai(i=1,2,…,n)是否处于环结构中,若ai不在环结构中,那么输出aii=2,执行步骤(2);否则输出aii=0,执行步骤(2);(2)再确定ai,i+1、ai+1,i(i=1,2,…,n-1)的值,在网N1中,依次计算ai与ai+1的行为关系,然后将行为关系转化为整数p,输出ai,i+1=ai+1,i=p,执行步骤(3);(3)再确定ai,i+2、ai+2,i(i=1,2,…,n-2)的值;若ai,i+1≠ai+1,i+2,输出ai,i+2=ai+2,i=min{ai,i+1,ai+1,i+2};否则,若ai,i+1=ai+1,i+2=1,那么输出ai,i+2=ai+2,i=1;否则,若ai,i+1=ai+1,i+2≠1,那么判断ai与ai+2的行为关系,并转化为行为关系数值q,输出ai,i+2=ai+2,i=q,执行步骤(4);(4)同理,确定ai,i+h、ai+h,i(i=1,2,…,n-h)(h=3…,n-1),输出ai,i+h=ai+h,i,直到最后一个元素a1n,算法终止;同理根据该行为关系矩阵图中元素的求解算法,计算出MDB中的元素bij(i,j=1,2,…,m),从而得到矩阵MDB。
- 如权利要求1所述的基于复杂对应系统的用户行为一致性度测量方法,其特征在于,步骤3-2中,所述计算用户模型与预期模型的行为一致性,其一致性度的求解算法为:输入:两个工作流网N1=(P1,T1;F1)和N2=(P2,T2;F2),他们的行为关系矩阵MDA0和MDB0,是由步骤3-1步骤中的行为关系矩阵图中元素的求解算法求得;输出:一致性度BP(1)先根据MDA0与MDB0中变迁集的对应关系,将MDA0和MDB0分别分为p和q个对应集合,将MDA0依次标记为{a1,a2,…am}、{am+1,am+2,…al}…{as+1,…an},执行步骤(2)(2)先根据MDA0中第一个与MDB0对应的集合{a1,a2,…am},取MDA0中前m阶方阵记为模块1,并执行步骤(3);(3)根据MDA0中第二个与MDB0对应的集合{am+1,am+2,…al},取MDA0中第1→(m)行和(m+1)→(l)列组成的m×(l-m)阶矩阵及其转置矩阵记为模块2,并执行步骤(4);(4)仿照之前的步骤,直到MDA0中第p个与MDB0对应的集合{as+1,…an},取MDA0中第1→(m)行和(s+1)→(n)列组成的m×(n-s)阶矩阵及其转置矩阵记为模块p,并执行步骤(5);(5)根据MDA0中第二个与MDB0对应的集合{am+1,am+2,…al},取MDA0中第(m+1)→(l)行和(m+1)→(l)列组成的(l-m)阶方阵记为模块p+1,并执行步骤(6);(6)仿照步骤(4),将MDA0中(m+1)→(l)行和(s+1)→(n)列组成的(l-m)×(n-s)阶矩阵及其转置矩阵记为模块p+2,并执行步骤(7);
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CN111242593B (zh) * | 2020-01-09 | 2022-05-31 | 东华大学 | 基于伙伴矩阵的交易系统的重叠对应行为一致性检测方法 |
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US20120137367A1 (en) * | 2009-11-06 | 2012-05-31 | Cataphora, Inc. | Continuous anomaly detection based on behavior modeling and heterogeneous information analysis |
CN103559588A (zh) * | 2013-11-15 | 2014-02-05 | 安徽理工大学 | 基于Petri网行为轮廓的日志挖掘方法 |
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US20120137367A1 (en) * | 2009-11-06 | 2012-05-31 | Cataphora, Inc. | Continuous anomaly detection based on behavior modeling and heterogeneous information analysis |
CN103559588A (zh) * | 2013-11-15 | 2014-02-05 | 安徽理工大学 | 基于Petri网行为轮廓的日志挖掘方法 |
CN104133808A (zh) * | 2014-07-10 | 2014-11-05 | 同济大学 | 基于复杂对应系统的用户行为一致性度测量方法 |
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