CN114285601A - Multi-dense-block detection and extraction method for big data - Google Patents
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Abstract
The invention discloses a method for detecting and extracting multiple dense blocks of big data, and aims to solve the technical problems of low detection accuracy and low recall rate of a dense block detection method in the prior art. It includes: acquiring K-dimensional tensor data D, the number m of dense blocks to be extracted and the size range of the dense blocks; measuring the suspicious degree of the K-dimensional tensor data D by using a density tracking coefficient based on a piecewise function, and generating a snapnotes list according to the suspicious degree and the size range of the dense block; m dense blocks are extracted from the K-dimensional tensor data D according to the snapshotts list. The invention can effectively improve the accuracy and recall rate of the dense blocks while ensuring the detection efficiency.
Description
Technical Field
The invention relates to a method for detecting and extracting multiple dense blocks of big data, and belongs to the technical field of abnormal data detection.
Background
With the advent of the big data age, detection of abnormal data is more and more important, and abnormal data containing network attacks usually have "consistency", such as: a group of IP addresses send requests to a plurality of fixed ports of a group of target IP at the same time interval; fraudulent data from purchasing zombie powder to increase influence would reveal a high degree of consistency among users of interest to a particular group of users. By establishing a tensor model, the consistency of the abnormal data can enable the tensor data to have dense blocks, so that the functions of detecting the abnormal data such as network attack detection, social network zombie powder detection and the like can be realized by detecting and extracting the dense blocks in the tensor data.
At present, the dense block detection and extraction method for the tensor model mainly comprises the following steps:
1. the dense block detection method based on tensor decomposition specifically includes methods such as hovvd and CP decomposition, which, although detecting dense blocks, do not have high ductility under density index and do not provide reasonable boundaries.
2. The cross spot detection method based on the Suspiousness provides a Suspiousness measurement index for measuring the suspicious degree of a dense block, and provides a cross spot method for detecting the dense block in a tensor, but the method can only detect a single dense block, and if the tensor contains a plurality of dense blocks with the same scale, a plurality of dense blocks can be combined into a large dense block, so that the accuracy and recall rate of the detection result are low.
3. The technology provides a dense block detection framework, which is suitable for different measurement indexes, can realize multi-dense block detection, and still cannot solve the problem that detection accuracy and recall rate are reduced due to the fact that dense blocks of the same scale are combined.
4. A suspicious block detection method based on a binary tree is characterized in that dense blocks detected each time are placed on a left leaf based on the binary tree, the dense blocks are regarded as common tensors to be further decomposed, meanwhile, a mathematical measurement method is designed to determine whether the dense blocks of the leaves are decomposed or not, if the dense blocks meet mathematical measurement rules, the decomposition is continued, if the dense blocks do not meet the mathematical measurement rules, the decomposition is stopped, and the like is carried out until all leaf nodes are decomposed. The method solves the problem of merging the dense blocks in the same specification, but has two problems: firstly, decomposition is needed to be carried out at each time, and then whether the termination condition is met is judged, so that the efficiency is very low; secondly, since a decomposition method is adopted, the first k dense blocks found cannot be guaranteed to be the k dense blocks with the maximum suspicious degree.
Disclosure of Invention
Aiming at the problems of low detection accuracy and low recall rate of the existing dense block detection method, the invention provides a method for detecting and extracting multiple dense blocks of big data.
In order to solve the technical problems, the invention adopts the following technical means:
the invention provides a method for detecting and extracting multiple dense blocks of big data, which comprises the following steps:
acquiring K-dimensional tensor data D, the number m of dense blocks to be extracted and the size range of the dense blocks;
measuring the suspicious degree of the K-dimensional tensor data D by using a density tracking coefficient based on a piecewise function, and generating a snapnotes list according to the suspicious degree and the size range of the dense block;
m dense blocks are extracted from the K-dimensional tensor data D according to the snapshotts list.
Further, the method for measuring the suspicious degree of the K-dimensional tensor data D by using the density tracking coefficient based on the piecewise function comprises the following steps:
taking K-dimensional tensor data D as input data;
deleting the column with the least count under each dimension of the input data to obtain the residual block b of each dimensioniWherein i represents dimension, i ═ 1,2, …, K;
computing the residual blocks b for each dimension based on a piecewise functioniA density tracking coefficient of;
computing each dimension's residual block b from density tracking coefficientsiThe DTS value for the degree of suspicious of the input tensor data D.
Further, the density tracking coefficient is expressed as follows:
wherein the content of the first and second substances,
residual block b representing the ith dimensioniThe density tracking coefficient of (a) is,
representing the remaining block biThe total count of (a) is counted,
representing the remaining block biThe product of the sizes of (a).
Further, the calculation formula of the suspicious degree DTS value is as follows:
wherein the content of the first and second substances,
residual block b representing the ith dimensioniThe value of the DTS of (a),
represents the Suspiobiousness metric index, NiDenotes a product of sizes in the ith dimension of the original K-dimensional tensor data D, and C denotes an overall count of the original K-dimensional tensor data D.
Further, the method for generating the snapnotes list according to the suspicious degree and the dense block size range comprises the following steps:
comparing the remaining blocks b of each dimensioniThe residual block b with the highest DTS value in each dimension is obtainedmax；
Judging the remaining block bmaxWhether the dense block size range is satisfied, if so, the remaining blocks bmaxAs data snapshot B;
storing the data snapshot B and the DTS value into a snapnotes list;
will remain block bmaxAnd as new input data, carrying out the suspicious degree measurement and the data snapshot extraction again until the input data is empty, and obtaining a final snapshotts list.
Further, the method for extracting m dense blocks from the K-dimensional tensor data D according to the snapnotes list comprises the following steps:
finding out a data snapshot Bmax with the maximum DTS value from a snapshotts list as a dense block;
deleting the data snapshot Bmax from the K-dimensional tensor data D to obtain updated tensor data D;
and generating a new snapshotts list according to the updated tensor data D, and extracting new dense blocks until m dense blocks are extracted.
The following advantages can be obtained by adopting the technical means:
the invention provides a method for detecting and extracting multiple dense blocks of big data, aiming at high-dimensional tensor data, the data blocks in the high-dimensional tensor data are tracked in real time through a designed density tracking coefficient and a DTS value to obtain the suspicious degree of the data blocks, all suspicious data snapshots are uniformly stored in a snapnotes list in the dense block detection process, dense blocks are selected one by one according to the list, the dense blocks cannot be spliced or split, the detection efficiency is ensured, and the accuracy and the recall rate of the dense blocks can be effectively improved. The method fully considers the density problem of the data blocks, and the density tracking coefficient can enable the residual blocks with low density to obtain lower DTS and enable the residual blocks with high density to obtain higher DTS values, so that the residual blocks with low density are removed in the subsequent extraction process, and the accuracy and the recall rate of the detection of the dense blocks are further improved.
Drawings
FIG. 1 is a flow chart of a big data multiple dense block detection and extraction method of the present invention;
FIG. 2 is a flow chart of single dense block detection and extraction in the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the accompanying drawings as follows:
the invention provides a method for detecting and extracting multiple dense blocks of big data, which specifically comprises the following steps as shown in figures 1 and 2:
step A, obtaining K-dimension tensor data D, wherein the specific operation comprises 4 steps: 1. integrating data, namely integrating the big data to be detected into a specified data center through an ETL technology; 2. desensitizing data, namely desensitizing sensitive information (such as identification numbers) in a production environment by a desensitizing technology; 3. data cleaning, namely cleaning the desensitized data to ensure the accuracy and consistency of the data; 4. and (3) data preprocessing, namely performing data modeling on the relational data obtained in the production environment and converting the relational data into K-dimensional tensor data D.
Manually setting the number m of the dense blocks to be extracted and the size range [ min, max ] of the dense blocks, wherein min is the lower limit of the size of the dense blocks, and max is the upper limit of the size of the dense blocks.
Step B, performing suspicious degree measurement on the K-dimensional tensor data D by using a density tracking coefficient based on a piecewise function, and generating a snapnotes list according to the suspicious degree and the dense block size range, wherein the specific operation is as follows:
and step B01, taking the K-dimensional tensor data D as input data, and establishing an empty list snapnotes for storing snapshots generated in each step of the tensor data D in the optimizing process.
Step B02, calculating the count of each column under each dimension of the input data, deleting the column with the least count, and obtaining the residual block B of each dimensioniI denotes dimension, i ═ 1,2, …, K.
The input data is assumed to be a two-dimensional tensor matrix of 100 multiplied by 100, the dimension K of the matrix is 2, the first dimension is a row, and the second dimension is a column; adding 100 elements of each row in the matrix to obtain the count of each row, comparing the counts of 100 rows, selecting the row with the least count, and removing the row with the least count from the 100 rows to obtain a 99-by-100 two-dimensional tensor matrix, which is the residual block b of the first dimension1(ii) a The same method is used to remove the least counted column from 100 columns to obtain a 100 by 99 two-dimensional tensor matrix, which is the remaining block b in the second dimension2。
Step B03, in order to avoid the problems that the accuracy and recall rate are reduced due to the fact that dense blocks with the same scale are combined, the density tracking coefficient based on the piecewise function is designed, and the expression of the density tracking coefficient is as follows:
wherein the content of the first and second substances,
residual block b representing the ith dimensioniThe density tracking coefficient of (a) is,
representing the remaining block biIs the total count of the remaining blocks biThe sum of all the elements in (A) and (B),
representing the remaining block biThe product of the sizes of (a).
Assuming a residual block size of 50 x 50, then
Is the sum of the 2500 elements in the remaining block,
K＝2。
sequentially calculating the residual block b of each dimension by using formula (3)iThe density tracking coefficient of (1).
Step B04, calculating the residual block B of each dimension according to the density tracking coefficientiFor the suspicious degree DTS value of the input tensor data D, the calculation formula is as follows:
wherein the content of the first and second substances,
residual block b representing the ith dimensioniThe value of the DTS of (a),
represents the Suspiobiousness metric index, NiDenotes a product of sizes in the i-th dimension of the original K-dimensional tensor data D, and C denotes a total count of the original K-dimensional tensor data D, i.e., a sum of all elements in the K-dimensional tensor data D.
Suspiciousness is a metric method proposed by previous scholars, which assumes that the original input tensor conforms to a Poisson distribution and evaluates the likelihood of the probability of a dense block appearing under the Poisson distribution.
Step B05, comparing the residual blocks B of each dimensioniThe residual block b with the highest DTS value in each dimension is obtainedmaxJudging the remaining block bmaxWhether or not the dense block size range, i.e. min, is satisfied<size(bmax)<max, if satisfied, will remain block bmaxAnd storing the data snapshot B and the DTS value into a snapnotes list, otherwise, not storing the data snapshot B.
Step B06, the rest blocks B in the step B05maxAnd (4) as new input data, repeating the steps B02-B05 until the input data are empty, stopping circulation, and obtaining a snapnotes list containing a plurality of data snapshots and corresponding DTS values.
Assume the remaining blocks b of the first dimension1(two-dimensional tensor matrix of 99 times 100) remaining blocks b having DTS values greater than the second dimension2(100 times 99 two-dimensional tensor matrix) selecting b1As bmax(ii) a When the above steps are repeated, b1(99 times 100 two-dimensional tensor matrix) as the new input data, from b1The least counted row of the 99 rows of (a) is removed to obtain a 98 by 100 two-dimensional tensor matrix from b1The least counted column is removed from the 100 columns to obtain a 99 by 99 two-dimensional tensor matrix, and the DT of the 98 by 100 two-dimensional tensor matrix is compared with the 99 by 99 two-dimensional tensor matrixThe value of S; and so on.
And C, extracting m dense blocks from the K-dimensional tensor data D according to the snapshotts list.
And step C01, acquiring the snapshotts list generated in the step B06, and finding out the data snapshot Bmax with the maximum DTS value from the snapshotts list to serve as a dense block.
And step C02, deleting the data snapshot Bmax from the K-dimensional tensor data D to obtain the updated tensor data D.
And C03, repeating the steps B to C02 by using the updated tensor data D, continuously updating the snapshotts list, extracting the dense blocks, updating the tensor data D until m dense blocks are extracted, and finally outputting the m dense blocks.
Compared with 4 prior arts in the background art, the invention has the following advantages:
1. the dense block detection method based on tensor decomposition is an earlier technology, and has poor extensibility on high-dimensional data, but the method can process the high-dimensional data and has high extensibility on the high-dimensional data.
2. The cross spot detection method based on Suspiousness and the M-Zoom dense fast detection framework have the problem that dense blocks with the same specification are combined, and a large dense block after splicing generates a plurality of non-dense areas, so that the recall rate of the detected dense block is low. If there is a matrix of 100 by 100 with two dense blocks, one is a small block of 20 by 20 with row and column marks from 1 to 20, and the other is a small block of 21 to 40 with row and column marks, if dense block detection is performed by using the CrossSpot detection method based on Suspiciousness or the M-Zoom dense block detection framework, a large dense block of 40 by 40 with the two small dense blocks as diagonal matrix (a block of 1 to 40 with row and column marks) is detected, and the upper right corner and the lower left corner of the large dense block are non-dense, resulting in a recall rate of only about 50% for the large dense block, and if there are 3 such small blocks, the recall rate may be only about 33%, and so on.
In the suspicious degree calculation process, the density problem is fully considered, the density tracking coefficient can enable the residual blocks with low density to obtain lower DTS and the residual blocks with high density to obtain higher DTS values, so that the residual blocks with low density are removed in the subsequent extraction process, and in the above example, the method can find two dense blocks with 20 times 20 (rather than directly 40 times 40).
3. Although the suspicious block detection method based on the binary tree can solve the problem of merging dense blocks of the same size, the efficiency is low, and because a large dense block is split firstly in judgment, the method is likely to split a large dense block with higher suspicious degree into 2 small dense blocks with insufficient suspicious degree, so that the finally extracted dense block is not the most suspicious. The method has the advantages that the problem does not exist, the DTS value of the residual block is calculated in real time through the density tracking coefficient, the dense block is detected in real time, the splitting operation does not exist, the efficiency is higher, and the true most suspicious block can be accurately found.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (6)
1. A big data multi-density block detection and extraction method is characterized by comprising the following steps:
acquiring K-dimensional tensor data D, the number m of dense blocks to be extracted and the size range of the dense blocks;
measuring the suspicious degree of the K-dimensional tensor data D by using a density tracking coefficient based on a piecewise function, and generating a snapnotes list according to the suspicious degree and the size range of the dense block;
m dense blocks are extracted from the K-dimensional tensor data D according to the snapshotts list.
2. The method for detecting and extracting multiple dense blocks of big data according to claim 1, wherein the method for measuring the suspicious degree of the K-dimensional tensor data D by using the density tracking coefficient based on the piecewise function comprises:
taking K-dimensional tensor data D as input data;
deleting the column with the least count under each dimension of the input data to obtain the residual block b of each dimensioniWherein i represents dimension, i ═ 1,2, …, K;
computing the residual blocks b for each dimension based on a piecewise functioniA density tracking coefficient of;
computing each dimension's residual block b from density tracking coefficientsiThe DTS value for the degree of suspicious of the input tensor data D.
3. The method for detecting and extracting multiple dense blocks of big data according to claim 2, wherein the expression of the density tracking coefficient is as follows:
wherein the content of the first and second substances,
residual block b representing the ith dimensioniThe density tracking coefficient of (a) is,
representing the remaining block biThe total count of (a) is counted,
representing the remaining block biThe product of the sizes of (a).
4. The method according to claim 2, wherein the suspicious degree DTS value is calculated as follows:
wherein the content of the first and second substances,
residual block b representing the ith dimensioniThe value of the DTS of (a),
represents the Suspiobiousness metric index, NiDenotes a product of sizes in the ith dimension of the original K-dimensional tensor data D, and C denotes an overall count of the original K-dimensional tensor data D.
5. The method of claim 2, wherein the method of generating the snapnotes list according to the suspicious degree and the range of the dense block sizes comprises:
comparing the remaining blocks b of each dimensioniThe residual block b with the highest DTS value in each dimension is obtainedmax；
Judging the remaining block bmaxWhether the dense block size range is satisfied, if so, the remaining blocks bmaxAs data snapshot B;
storing the data snapshot B and the DTS value into a snapnotes list;
will remain block bmaxAnd as new input data, carrying out the suspicious degree measurement and the data snapshot extraction again until the input data is empty, and obtaining a final snapshotts list.
6. The method for detecting and extracting multiple dense blocks of big data according to claim 1 or 5, wherein the method for extracting m dense blocks from the K-dimensional tensor data D according to the snapshotts list comprises:
finding out a data snapshot Bmax with the maximum DTS value from a snapshotts list as a dense block;
deleting the data snapshot Bmax from the K-dimensional tensor data D to obtain updated tensor data D;
and generating a new snapshotts list according to the updated tensor data D, and extracting new dense blocks until m dense blocks are extracted.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013081650A1 (en) * | 2011-11-28 | 2013-06-06 | Hewlett-Packard Development Company, L. P. | Clustering event data by multiple time dimensions |
| CN108366048A (en) * | 2018-01-10 | 2018-08-03 | 南京邮电大学 | A kind of network inbreak detection method based on unsupervised learning |
| CN109064189A (en) * | 2018-07-13 | 2018-12-21 | 北京亚鸿世纪科技发展有限公司 | Brush list detecting and alarm device based on the detection of intensive block |
| CN109753797A (en) * | 2018-12-10 | 2019-05-14 | 中国科学院计算技术研究所 | For the intensive subgraph detection method and system of streaming figure |
| CN110322356A (en) * | 2019-04-22 | 2019-10-11 | 山东大学 | The medical insurance method for detecting abnormality and system of dynamic multi-mode are excavated based on HIN |
| CN111523012A (en) * | 2019-02-01 | 2020-08-11 | 慧安金科（北京）科技有限公司 | Method, apparatus, and computer-readable storage medium for detecting abnormal data |
| US20210150305A1 (en) * | 2019-11-19 | 2021-05-20 | Ciena Corporation | Forecasting time-series data in a network environment |
| CN113420608A (en) * | 2021-05-31 | 2021-09-21 | 高新兴科技集团股份有限公司 | Human body abnormal behavior identification method based on dense space-time graph convolutional network |
-
2021
- 2021-11-24 CN CN202111406838.7A patent/CN114285601B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013081650A1 (en) * | 2011-11-28 | 2013-06-06 | Hewlett-Packard Development Company, L. P. | Clustering event data by multiple time dimensions |
| CN108366048A (en) * | 2018-01-10 | 2018-08-03 | 南京邮电大学 | A kind of network inbreak detection method based on unsupervised learning |
| CN109064189A (en) * | 2018-07-13 | 2018-12-21 | 北京亚鸿世纪科技发展有限公司 | Brush list detecting and alarm device based on the detection of intensive block |
| CN109753797A (en) * | 2018-12-10 | 2019-05-14 | 中国科学院计算技术研究所 | For the intensive subgraph detection method and system of streaming figure |
| CN111523012A (en) * | 2019-02-01 | 2020-08-11 | 慧安金科（北京）科技有限公司 | Method, apparatus, and computer-readable storage medium for detecting abnormal data |
| CN110322356A (en) * | 2019-04-22 | 2019-10-11 | 山东大学 | The medical insurance method for detecting abnormality and system of dynamic multi-mode are excavated based on HIN |
| US20210150305A1 (en) * | 2019-11-19 | 2021-05-20 | Ciena Corporation | Forecasting time-series data in a network environment |
| CN113420608A (en) * | 2021-05-31 | 2021-09-21 | 高新兴科技集团股份有限公司 | Human body abnormal behavior identification method based on dense space-time graph convolutional network |
Non-Patent Citations (4)
| Title |
|---|
| KIJUNG SHIN ET AL.: "D-Cube: Dense-Block Detection in Terabyte-Scale Tensors", 《ACM 》 * |
| KIJUNG SHIN ET AL.: "M-Zoom: Fast Dense-Block Detection", 《SPRINGER INTERNATIONAL PUBLISHING AG 2016》 * |
| 杨锐等: "一种基于深度学习的异常行为识别方法", 《五邑大学学报(自然科学版)》 * |
| 范卫俊等: "张量数据中的多密集块检测方法", 《计算机应用研究》 * |
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