Modeling and detecting method of behavior sequence based on HDP-HMM

A modeling method and behavioral technology, applied in the field of behavioral sequence modeling and detection, can solve the problems of parameter overfitting, imperfection, lack of adaptability of the model, etc., and achieve good detection effect

Active Publication Date: 2018-08-28
XIDIAN UNIV
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Problems solved by technology

HMM is greatly limited in the definition of model structure and in the standard estimation method of model parameters, and there are many imperfections in the solution of many practical problems.
For example, the maximum likelihood estimation method does not fully consider the complexity of the model, which is likely to cause overfitting of the parameters
In addition, the model structure of traditional HMM must be determined in advance, that is, the observed value and hidden state of the model need to be determined in advance, which makes the model lack of adaptability

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  • Modeling and detecting method of behavior sequence based on HDP-HMM
  • Modeling and detecting method of behavior sequence based on HDP-HMM
  • Modeling and detecting method of behavior sequence based on HDP-HMM

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Embodiment Construction

[0029] The present invention will be further described below in conjunction with specific embodiments. The exemplary embodiments and descriptions of the present invention are used to explain the present invention, but not as a limitation to the present invention.

[0030] Such as figure 1 As shown, a kind of behavioral sequence modeling method based on HDP-HMM of the present embodiment uses the HDP-HMM model to model the behavioral sequence and uses the Beam Sampling algorithm to automatically iterate out the model parameters. The specific steps are as follows:

[0031] S1, establish the HDP-HMM model, select the object sequence data of the HTTP request of the user to the server as the observation to describe the behavior of the user;

[0032] S2. Preprocessing the data, and dividing the object sequence data in step S1 into training data and test data;

[0033] S3. Express the set of all HTTP request sequences in the training data as y={y 1 ,...y T};

[0034] S4. Assign a ...

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Abstract

The invention discloses a modeling and detecting method of a behavior sequence based on HDP-HMM. A logarithmic transition probability of a state trajectory is used as a judgment index, thereby being simpler, two models are established to jointly determine the detection result from the positive and negative angles, and the problem that judgment index parameters are too complex to be selected and calculated in the previous detection method is solved. Compared with the prior art, the modeling and detecting method disclosed by the invention has the advantages of obtaining a better detection resultwhile compensating the defects of the HMM on model definition and parameter estimation, and it can be seen from experiments that the average detection rate of the modeling and detection method in theinvention is 95.3%.

Description

technical field [0001] The invention relates to the field of behavior pattern analysis, in particular to a method for modeling and detecting behavior sequences based on HDP-HMM. Background technique [0002] HMM is widely used in many fields and has good performance, but it still has some shortcomings. HMM is limited both in the definition of the model structure and in the standard estimation method of the model parameters, and there are many imperfections in the solution of many practical problems. For example, the maximum likelihood estimation method does not fully consider the complexity of the model, which is likely to cause over-fitting of the parameters. In addition, the model structure of traditional HMM must be determined in advance, that is, the observed value and hidden state of the model need to be determined in advance, which makes the model lack of adaptability. [0003] In response to these shortcomings of HMM, Beal et al. applied the Hierarchical Dirichlet P...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/06H04L29/08G06K9/62
CPCH04L63/14H04L67/02G06F18/295
Inventor 陈岱
Owner XIDIAN UNIV
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