User abnormal behavior detection method based on neural network clustering

A neural network and behavioral technology, applied in the field of information security credibility, can solve problems such as high false alarm rate, achieve the effect of improving decomposition speed and signal-to-noise ratio

Active Publication Date: 2016-01-06
HENAN UNIV OF SCI & TECH
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Problems solved by technology

But this technology also has disadvantages, its false alarm rate is relatively high

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  • User abnormal behavior detection method based on neural network clustering
  • User abnormal behavior detection method based on neural network clustering
  • User abnormal behavior detection method based on neural network clustering

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Embodiment Construction

[0028] As shown in the figure, a method for detecting abnormal user behavior based on neural network clustering is characterized in that it includes the following steps:

[0029] Step 1. Assuming that each user is an object, the behavior data of the object is used as the attribute of the object, and the matrix real matrix A of n*m dimensions is expressed as an object-attribute structure, and the matrix A is expressed as ,in, Expressed as the jth attribute of the ith object;

[0030] Step 2. Use the matrix A in step 1 to use the formula A=USV T Perform SVD decomposition, where U=(u 1 ,u 2 ,...,u m ), V=(v 1 ,v 2 ,...,v n ), , , (i=1,2,...,r), for A T All the non-zero eigenvalues ​​of A, arranged in descending order, are the singular values ​​of A;

[0031] Step 3. Calculate the effective rank order of the decomposed SVD matrix according to the following formulas (1)-(3) to denoise user behavior information

[0032] (1),

[0033] (2),

[0034] (3), wh...

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Abstract

The invention discloses a user abnormal behavior detection method based on neural network clustering. The method comprises: firstly, performing SVD decomposition and denoising on a behavior data set matrix of a user, and inputting the denoised matrix to an input layer of a neural network, and then calculating weight of all attributes of each user in a hidden layer of the neural network, and standardizing the weight, outputting the weight in an output layer, and finally respectively calculating similarity value of each user and each user in a normal behavior model database and a threshold value, if the similarity value is larger than the threshold value, behavior being abnormal behavior, and carrying out corresponding prompt and precautionary measures, otherwise, the behavior being normal behavior, and combining the behavior in the normal model database to upgrade the database in real time. Through cooperation of each part, the method effectively realizes high detection rate and low false alarm rate.

Description

technical field [0001] The invention relates to the technical field of information security credibility, in particular to a method for detecting user abnormal behavior based on neural network clustering. Background technique [0002] Today is in the era of big data, all kinds of user information are stored in the computer, which brings convenience to people's work and life, but also makes information security face a severe test. With the popularization of computers, various hackers and intrusions emerge in endlessly , with the network attack technology becoming more mature and changing, the traditional passive defense methods obviously cannot solve the security problem of user information well. For various passive defense measures, people are more inclined to active detection technology, and adopt Graph clustering is a popular method for detecting abnormal user behavior. [0003] Using the method of graph clustering to realize the identification and detection of user abnorm...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/56G06K9/62
CPCG06F21/566G06F18/24
Inventor 郑瑞娟张明川吴庆涛白秀玲魏汪洋赵海霞陈京张童王成磊杨丽
Owner HENAN UNIV OF SCI & TECH
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