Apparatus, method and program for detecting abnormal behavior
First Claim
1. A probabilistic distribution estimation apparatus for responding to, as input data, a string of vector data to estimate a probabilistic distribution occurred in each data by successively reading said string of vector data, said probabilistic distribution estimation apparatus comprising:
- a parameter storage unit for storing all of parameters for the stochastic model having the hidden variables;
certainty calculation means for calculating, in response to said input data, a certainty where said input data occurs using said stochastic model by reading the parameters of said stochastic model from said parameter storage unit;
parameter renewal means for renewing contents of said parameter storage unit in accordance with new read data with past data forgotten by reading the certainty from said certainty calculation means and by reading each parameter of said stochastic model from said parameter storage unit; and
outputting means for outputting several parameters of said stochastic model stored in said parameter storage unit.
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Abstract
Supplied with a string of vector data as input data, a probabilistic distribution estimation apparatus estimates, by using a stochastic model having hidden variables, a probabilistic distribution in which each data occurs by successively reading the train of vector data. Specifically, the probabilistic distribution estimation apparatus reads values of parameters of the stochastic model having the hidden variables for a value of the input data, calculates, by using the stochastic model, a certainty in which the input data occurs, renews the parameters in response to new read data with past data forgotten, and produce several parameter'"'"'s values. By using the parameter'"'"'s values received from the probabilistic distribution estimation apparatus, an abnormality detection unit calculates an information amount of data as an abnormal behavior degree to produce the abnormal behavior degree.
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Citations
20 Claims
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1. A probabilistic distribution estimation apparatus for responding to, as input data, a string of vector data to estimate a probabilistic distribution occurred in each data by successively reading said string of vector data, said probabilistic distribution estimation apparatus comprising:
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a parameter storage unit for storing all of parameters for the stochastic model having the hidden variables;
certainty calculation means for calculating, in response to said input data, a certainty where said input data occurs using said stochastic model by reading the parameters of said stochastic model from said parameter storage unit;
parameter renewal means for renewing contents of said parameter storage unit in accordance with new read data with past data forgotten by reading the certainty from said certainty calculation means and by reading each parameter of said stochastic model from said parameter storage unit; and
outputting means for outputting several parameters of said stochastic model stored in said parameter storage unit. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method of estimating a probabilistic distribution, comprising the steps of:
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inputting a string of vector data as input data;
calculating, using a model in which each data occurs by successively reading the string of vector data, a certainty for a value of the input data in which said input data occurs on the basis of parameters of said stochastic model;
renewing, by using said certainty and the parameters of said stochastic model, the parameters in response to new read data with past data forgotten; and
outputting several values of the calculated parameters. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14)
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15. A probabilistic distribution estimation program for making a computer respond to, as input data, a string of vector data to estimate a probabilistic distribution occurred in each data by successively reading said string of vector data, said probabilistic distribution estimation program making said computer operate as:
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a parameter storage unit for storing all of parameters for the stochastic model having the hidden variables;
certainty calculation means for calculating, in response to said input data, a certainty where said input data occurs using said stochastic model by reading the parameters of said stochastic model from said parameter storage unit;
parameter renewal means for renewing contents of said parameter storage unit in accordance with new read data with past data forgotten by reading the certainty from said certainty calculation means and by reading each parameter of said stochastic model from said parameter storage unit; and
outputting means for outputting several parameters of said stochastic model stored in said parameter storage unit. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification