System and method for abnormality detection
First Claim
1. A method for use in abnormality detection, the method comprising:
- providing a plurality of neural network modules; and
sequentially training said neural network modules in a cascade order with respect to a set of training data pieces to thereby reduce an error value for each successive neural network module, thereby minimizing an error value with respect to said training data pieces for each successive neural network module in the cascade;
wherein said sequentially training comprising;
a. providing a first set of calibration data pieces and generating therefrom a first normal behavior map corresponding to said first set of calibration data,b. determining a second set of calibration data pieces which includes calibration data pieces of said first set of calibration data pieces being outside of said first normal behavior map,c. using said second set of calibration data pieces and generating a corresponding second normal behavior map corresponding to said second set of calibration data,d. determining a third set of calibration data pieces including calibration data pieces being outside of said second normal behavior map,e. repeating the process until number of calibration data pieces in a newly generated set satisfies at least one of the following;
the number equals to number of calibration data pieces in previous set, or the number is below a predetermined threshold.
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Accused Products
Abstract
A system and method for use in data analysis are provided. The system comprises a data processing utility configured to receive and process input data, comprising: plurality of neural network modules capable for operating in a training mode and in a data processing mode in accordance with the training; a network training utility configured for operating the neural network modules in the training mode utilizing selected set of training data pieces for sequentially training of the neural network modules in a cascade order to reduce an error value with respect to the selected set of the training data pieces for each successive neural network module in the cascade; and an abnormality detection utility configured for sequentially operating said neural network modules for processing input data, and classifying said input data as abnormal upon identifying that all the neural network modules provide error values being above corresponding abnormality detection thresholds.
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Citations
5 Claims
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1. A method for use in abnormality detection, the method comprising:
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providing a plurality of neural network modules; and sequentially training said neural network modules in a cascade order with respect to a set of training data pieces to thereby reduce an error value for each successive neural network module, thereby minimizing an error value with respect to said training data pieces for each successive neural network module in the cascade; wherein said sequentially training comprising; a. providing a first set of calibration data pieces and generating therefrom a first normal behavior map corresponding to said first set of calibration data, b. determining a second set of calibration data pieces which includes calibration data pieces of said first set of calibration data pieces being outside of said first normal behavior map, c. using said second set of calibration data pieces and generating a corresponding second normal behavior map corresponding to said second set of calibration data, d. determining a third set of calibration data pieces including calibration data pieces being outside of said second normal behavior map, e. repeating the process until number of calibration data pieces in a newly generated set satisfies at least one of the following;
the number equals to number of calibration data pieces in previous set, or the number is below a predetermined threshold. - View Dependent Claims (2, 3, 4, 5)
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Specification