CN109064189A - Brush list detecting and alarm device based on the detection of intensive block - Google Patents
Brush list detecting and alarm device based on the detection of intensive block Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
- G06Q30/0635—Processing of requisition or of purchase orders
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Abstract
Brush list detecting and alarm based on the detection of intensive block is related to information technology field, and the present invention is by dimension definer, behavior definer, data connector, intensive block detector composition；Wherein intensive block detector is made of local search device and shaping modes algorithm；The single detecting and alarm device of brush based on the detection of intensive block of the invention uses based on a kind of new evaluation index the dense subgraph found on uncertain figure, by randomly selecting a block, then the dimension that this block is constantly adjusted using a kind of method for being similar to greed, until it reaches local optimum.Method of the invention is due to being to be compared excavation to dense subgraph in various dimensions, therefore the set all under i.e. multiple dimensions under multiple evaluation indexes with dense subgraph feature can more accurately be found, therefore false sale group can accurately and be effectively determined, malice brushes single group, with high accuracy, the feature of broad applicability.The present invention can be widely used in network invasion monitoring, false microblogging transfer amount detection, the analysis of corpse powder and science of heredity correlative study.
Description
Technical field
The present invention relates to the behavioural analysis fields based on big data in information technology field, especially information technology.
Background technique
With the fast development of e-commerce industry, shopping at network is increasingly becoming a kind of new life style, electric business industry
Competition also grow in intensity, under the driving of interests, electric business behavior has been increasingly becoming " underlying rule " of electric business platform, due to electric business
Just under development, various constrained qualifications are all not perfect for business, so the brush single act of electric business platform is than more serious.
For brush single act, the counter-brush single system in Jingdone district is counted from multiple dimensions such as order, commodity, user, logistics,
The different characteristic value under each dimension is calculated separately, can more precisely identify the malicious act of brush simple correlation.
The single auditing system of Taobao's backstage detection brush mainly includes that machine is examined and two aspects of manual examination and verification, for wherein machine
It examines the order for being difficult to judge and obtains final result into artificial investigation, hotel owner can appeal, and artificial judgement rank can be entered after complaint
Section, by checking that the content of commodity evaluation, Bidder Information are judged.
Detection method brush list detection in the prior art is only limitted to self supervise and examine mechanism of electric business platform interior, needs a large amount of
Insider transaction initial data carry out signature analysis or special data receiver and analysis needed to be equipped with, still can not be clear and accurate
Ground is analyzed with the presence or absence of brush single act.
Existing technology generally relies on complicated data model and analyzes brush single act, by obtaining multiple commodity
Initial data, needing to calculate a large amount of feature rate value includes silent conversion ratio, order consulting rate, traffic transformation rate, conclusion of the business conversion
Rate, order payment rate, collection rate, goods receiving time be poor, brush hand accounting, shop residence time, then by calculating optimal training pattern
It obtains the single probability of brush of the end article or needs to rely on special equipment reception and the dynamic for preventing brush single act is set
Password and key, be difficult rapidly to analyze in large batch of data basis the brush single act of group and to these behaviors into
The sequence of the suspicious degree of row.
For the brush single act of group, find after study the intensive block in tensor data often correspond to it is suspicious,
Synchronous behavior, for example, rubbish disseminator can repeat to write to restaurant or hotel it is same, high or low, advantageous to oneself
Evaluation, or with same user, even same text.Corpse powder can largely go to pay close attention to their customer, so that powder
Silk quantity can be very big.It is this it is highdensity generate be to be attributed to the same reason: rubbish disseminator can be limited to resource (user,
The address IP, timestamp etc.), but think to add more sides as far as possible in figure or tensor data to maximize pecuniary benefit.
It instinctively says, the behavior in data is more synchronous, and dimension is higher, the more worth further investigation of this data.
Intensive block in tensor model represents the synchronization behavior of a group user, these more intensive behaviors of density often more may be used
It doubts.Intensive block extraction in tensor can be applied to network invasion monitoring, the detection of false microblogging transfer amount, the analysis of corpse powder and
Science of heredity correlative study.The analysis that the present invention introduces the intensive block analysis technology solution network platform falseness brush list in tensor is asked
Topic.
The definition of intensive block: being M × N, density D in adjacency matrix A(size), the block (S, T) that a size is m × n
It can be called " intensive block ", and if only if density d (S, T) than uniform densityWant high, i.e. d (S, T) >=, whereinIt is square
The threshold densities of battle array.
It is calculated using intensive block to speculate false sales behavior mode, basic thought is that false sales behavior is in the graphic
Special connection mode can be presented in the proper subspace of the now connection mode of intensive behavior, adjacency matrix, pass through the special of presentation
Connection mode to analyze, sell, malice brushes single behavior by the falseness of the platforms such as shopping at network, e-commerce.
The method fast and accurately detected to the intensive block in tensor in the prior art has:
The first is that the intensive block based on tensor resolution excavates, and tensor resolution is applied to the excavation to intensive sub- tensor.Based on
There is some disadvantages for the intensive block method for digging that amount is decomposed: not considering the property of background data；Do not have under density index
High ductility；Reasonable boundary cannot be provided.
Second is dense subgraph method for digging, and the method that newest dense subgraph excavates mainly has: using maximum whole
Density and limited registration find the discovery of dense subgraph of the intensive subgraph based on nuclear decomposition, the disadvantages of the method are as follows obscurity boundary；
The dense subgraph on uncertain figure is found based on a kind of new evaluation index and is based on data flow or distributed dynamic
The excavation of state dense subgraph, this method is it needs to be determined that new evaluation index, and evaluation index has correlation with data flow, and uncomfortable
For all scenes.
Defect according to prior art, the brush list detecting and alarm device of the invention based on the detection of intensive block, which uses, is based on one
Kind new evaluation index finds the dense subgraph on uncertain figure, by randomly selecting a block, then using a kind of close
The method for being similar to greed constantly adjusts the dimension of this block, until it reaches local optimum.Method of the invention is due to being more
Dimension is compared excavation to dense subgraph, therefore can more accurately find under multiple evaluation indexes under i.e. multiple dimensions
All with the set of dense subgraph feature, therefore it can accurately and effectively determine false sale group, malice brushes single group,
With high accuracy, the feature of broad applicability.
Summary of the invention
Brush list detecting and alarm based on the detection of intensive block is by dimension definer, behavior definer, data connector, intensive block
Detector composition；Wherein intensive block detector is made of local search device and shaping modes algorithm；
Dimension definer is responsible for definition and does N-dimensional space when intensive block determines, and in brush single detection, the type of dimension includes but not
Be limited to: on-line shop address, blog address, hotel evaluate address, restaurant ratings address；When doing to on-line shop's address style, brush is single to be determined
When, dimension N refers to the on-line shop of N family in given area, and given area refers under same system that same system refers to range of management, such as day
Cat, Jingdone district, Alibaba belong to different range of managements；
Behavior definer is responsible for defining the type of intensive behavior, and in the single detection of brush, the type of intensive behavior includes but is not limited to:
It repeats to evaluate, same to evaluate, corpse powder is largely paid close attention to；
The variable in algorithm that data connector is responsible in intensive block detector corresponds to corresponding data type, each user
It can be expressed as the data point of N-dimensional, subspace is formed with two singular values, is the point of N-dimensional degree in space visual
Change.It utilizesThe point set that can be expressed as proper subspace is formed, whereinWhat is indicated is that nth user exists
Value in i-th left singular vector, right proper subspace is similar, can be expressed as, pass through dissipating in these spaces
Point diagram can be explained in user in the special relationship being directly connected to；Indicate that dimension j possesses Nj
Possible value；It is defined as the intersection for the probable value that dimension j can take in suspicious piece, wherein on each dimension j
Possible value is all the subset of the possible value of total data set in respective dimensions；Represent the quality of suspicious piece of possible value；Indicate suspicious piece of calculating function within the data block, wherein
F is evaluation function；
The local search device of intensive block detector is responsible for calculating in each dimension j in K dimension from a seed BOB(beginning of block)
Most probable value iterates to calculate the most probable value until obtaining suspicious piece, exports suspicious piece；Seed block support randomly selects
Mode and the specified mode chosen；The algorithmic notation of local search device is as follows:
Require:Data X, seedregion Y with
while not converged do
for j=1...K do
ADJUSTMODE(j)
end for
end while
return ；
ADJUSTMODE is the shaping modes algorithm of intensive block detector, and it is dimension j that shaping modes algorithm is calculative every time,
So it is constant to need to fix other dimensions dimension value in addition to j dimension in iteration；WhereinIt refers to tieing up
Spend value in jBring mass change.The complexity of algorithm is O (T × K × (E+NlogN)), and wherein T is the number of iterations, K
It is the number of dimension, E is the number of item non-zero in data set；
The function expression ADJUSTMODE of shaping modes algorithm is as follows:
Beneficial effect
Realize it is of the invention based on the single detecting and alarm of brush of intensive block detection compared to the anomalous identification in the past to individual, identification
It is group, a greater amount of discovery target group of energy are high-efficient by the real-time calculated result of computer.
Detailed description of the invention
Fig. 1 is frame construction drawing of the invention.
Specific embodiment
With reference to Fig. 1, the brush list detecting and alarm of the invention based on the detection of intensive block is realized by dimension definer 1, behavior is fixed
Adopted device 2, data connector 3, intensive block detector 4 form；Wherein intensive block detector 4 is by local search device 41 and shaping modes
Algorithm 42 forms；
Dimension definer 1 is responsible for definition and does N-dimensional space when intensive block determines, and in brush single detection, the type of dimension includes but not
Be limited to: on-line shop address, blog address, hotel evaluate address, restaurant ratings address；When doing to on-line shop's address style, brush is single to be determined
When, dimension N refers to the on-line shop of N family in given area, and given area refers under same system that same system refers to range of management, such as day
Cat, Jingdone district, Alibaba belong to different range of managements；
Behavior definer 2 is responsible for defining the type of intensive behavior, and in the single detection of brush, the type of intensive behavior includes but is not limited to:
It repeats to evaluate, same to evaluate, corpse powder is largely paid close attention to；
The variable in algorithm that data connector 3 is responsible in intensive block detector 4 corresponds to corresponding data type, each user
Can be expressed as the data point of N-dimensional, subspace is formed with two singular values, be the point of N-dimensional degree in space can
Depending on changing.It utilizesThe point set that can be expressed as proper subspace is formed, whereinWhat is indicated is n-th
Value of the user in i-th left singular vector, right proper subspace is similar, can be expressed as, pass through these skies
Between in scatter plot can explain in user in the special relationship being directly connected to；Indicate dimension
Degree j possesses Nj may value；It is defined as the intersection for the probable value that dimension j can take in suspicious piece,
In possibility value on each dimension j be in respective dimensions total data set may value subset；Represent suspicious piece
The quality of possible value；Indicate suspicious piece in number
According to the calculating function in block, wherein f is evaluation function；
The local search device 41 of intensive block detector 4 is responsible for from a seed BOB(beginning of block), in each dimension j in K dimension
Most probable value is calculated, the most probable value until obtaining suspicious piece is iterated to calculate, exports suspicious piece；Seed block supports random choosing
The mode and the specified mode chosen taken；The algorithmic notation of local search device is as follows:
Require:Data X, seedregion Y with
while not converged do
for j=1...K do
ADJUSTMODE(j)
end for
end while
return ；
ADJUSTMODE is the shaping modes algorithm 42 of intensive block detector 4, and shaping modes algorithm 42 is calculative every time to be
Dimension j, so it is constant to need to fix other dimensions dimension value in addition to j dimension in iteration；WhereinRefer to
It is the value in dimension jBring mass change.The complexity of algorithm is O (T × K × (E+NlogN)), and wherein T is iteration
Number, K are the numbers of dimension, and E is the number of item non-zero in data set；
The function expression ADJUSTMODE of shaping modes algorithm is as follows:
The present invention is applicable not only to the detection of brush single act, and the present invention can be widely used in network invasion monitoring, void
False microblogging transfer amount detection, the analysis of corpse powder and science of heredity correlative study.
Claims (1)
1. the brush list detecting and alarm based on the detection of intensive block, it is characterised in that by dimension definer, behavior definer, data connection
Device, intensive block detector composition；Wherein intensive block detector is made of local search device and shaping modes algorithm；
Dimension definer is responsible for definition and does N-dimensional space when intensive block determines, and in brush single detection, the type of dimension includes but not
Be limited to: on-line shop address, blog address, hotel evaluate address, restaurant ratings address；When doing to on-line shop's address style, brush is single to be determined
When, dimension N refers to the on-line shop of N family in given area, and given area refers under same system that same system refers to range of management, such as day
Cat, Jingdone district, Alibaba belong to different range of managements；
Behavior definer is responsible for defining the type of intensive behavior, and in the single detection of brush, the type of intensive behavior includes but is not limited to:
It repeats to evaluate, same to evaluate, corpse powder is largely paid close attention to；
The variable in algorithm that data connector is responsible in intensive block detector corresponds to corresponding data type, each user
It can be expressed as the data point of N-dimensional, subspace is formed with two singular values, is the point of N-dimensional degree in space visual
Change；
It utilizesThe point set that can be expressed as proper subspace is formed, whereinWhat is indicated is that nth user exists
Value in i-th left singular vector, right proper subspace is similar, can be expressed as, by these spaces
Scatter plot can be explained in user in the special relationship being directly connected to；Indicate that dimension j possesses Nj
A possibility value；It is defined as the intersection for the probable value that dimension j can take in suspicious piece, wherein each dimension j
On possibility value be all in respective dimensions total data set may value subset；Represent the matter of suspicious piece of possible value
Amount；Indicate suspicious piece of calculating function within the data block,
Wherein f is evaluation function；
The local search device of intensive block detector is responsible for calculating in each dimension j in K dimension from a seed BOB(beginning of block)
Most probable value iterates to calculate the most probable value until obtaining suspicious piece, exports suspicious piece；Seed block support randomly selects
Mode and the specified mode chosen；The algorithmic notation of local search device is as follows:
Require:Data X, seed region Ywith
while not converged do
for j=1...K do
ADJUSTMODE(j)
end for
end while
return ；
ADJUSTMODE is the shaping modes algorithm of intensive block detector, and it is dimension j that shaping modes algorithm is calculative every time,
So it is constant to need to fix other dimensions dimension value in addition to j dimension in iteration；WhereinIt refers in dimension j
Middle valueBring mass change；
The complexity of algorithm is O (T × K × (E+NlogN)), and wherein T is the number of iterations, and K is the number of dimension, and E is data set
In non-zero item number；
The function expression ADJUSTMODE of shaping modes algorithm is as follows:
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112085586A (en) * | 2020-08-10 | 2020-12-15 | 北京中亦安图科技股份有限公司 | Bank credit card cash register method based on intensive subgraph |
| CN112435068A (en) * | 2020-11-30 | 2021-03-02 | 北京沃东天骏信息技术有限公司 | Malicious order identification method and device, electronic equipment and storage medium |
| CN113139182A (en) * | 2021-05-17 | 2021-07-20 | 毕晓柏 | Data intrusion detection method for online e-commerce platform |
| CN114218610A (en) * | 2021-11-24 | 2022-03-22 | 南京信息职业技术学院 | Multi-dense block detection and extraction method based on Possion distribution |
| CN114285601A (en) * | 2021-11-24 | 2022-04-05 | 南京信息职业技术学院 | Multi-dense-block detection and extraction method for big data |
| CN118037387A (en) * | 2024-01-23 | 2024-05-14 | 深圳市尚微尔科技有限公司 | Transaction supervision system and method based on Internet |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112085586A (en) * | 2020-08-10 | 2020-12-15 | 北京中亦安图科技股份有限公司 | Bank credit card cash register method based on intensive subgraph |
| CN112085586B (en) * | 2020-08-10 | 2024-04-16 | 北京中亦安图科技股份有限公司 | Bank credit card anti-cash registering method based on dense subgraph |
| CN112435068A (en) * | 2020-11-30 | 2021-03-02 | 北京沃东天骏信息技术有限公司 | Malicious order identification method and device, electronic equipment and storage medium |
| CN113139182A (en) * | 2021-05-17 | 2021-07-20 | 毕晓柏 | Data intrusion detection method for online e-commerce platform |
| CN113139182B (en) * | 2021-05-17 | 2022-06-21 | 深圳市蜜蜂互联网络科技有限公司 | Data intrusion detection method for online e-commerce platform |
| CN114218610A (en) * | 2021-11-24 | 2022-03-22 | 南京信息职业技术学院 | Multi-dense block detection and extraction method based on Possion distribution |
| CN114285601A (en) * | 2021-11-24 | 2022-04-05 | 南京信息职业技术学院 | Multi-dense-block detection and extraction method for big data |
| CN114285601B (en) * | 2021-11-24 | 2023-02-14 | 南京信息职业技术学院 | Multi-dense-block detection and extraction method for big data |
| CN118037387A (en) * | 2024-01-23 | 2024-05-14 | 深圳市尚微尔科技有限公司 | Transaction supervision system and method based on Internet |
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