# US 11,113,394 B2 · Data type recognition, model training and risk recognition methods, apparatuses and devices

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- Number: US 11,113,394 B2
- Document ID: US11113394B2
- Status: Granted
- Kind: B2
- Application number: 16/444,156
- Filed: 2019-06-18
- Published: 2019-10-03
- Granted: 2021-09-07
- Earliest claimed: 2017-06-16
- Assignees: Advanced New Technologies Co., Ltd.
- Applicants: Advanced New Technologies Co., Ltd.
- Inventors: Yu Cheng
- Web page: https://patentgrep.com/patent/US11113394B2

## CPC

- G06F 21/552: {involving long-term monitoring or reporting}
- G06F 16/2465: {Query processing support for facilitating data mining operations in structured databases}
- G06F 16/35: Clustering; Classification
- G06F 18/24: Classification techniques
- G06N 20/00: Machine learning
- G06Q 10/0635: Risk analysis of enterprise or organisation activities
- G06F 2221/034: Test or assess a computer or a system
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Data type recognition and model training methods and apparatuses, and computer devices are provided. The model training method includes acquiring a first sample data set, and using the first sample data set to train an anomaly detection model; and detecting an abnormal sample data set from a second sample data set by means of the anomaly detection model, and using the abnormal sample data set to train a classification model. By using this method, an amount of scoring events of the classification model can be reduced, and relatively balanced sample data sets can also be provided for training, to obtain the classification model with a higher accuracy.

## Claims

### Claim 1

A data type recognition method for recognizing data as first-type data or second-type data, wherein the method comprises:
acquiring data to be recognized, and using a preset anomaly detection model to detect whether the data to be recognized is first-type data; and
inputting other data than the first-type data recognized by the anomaly detection model, into a classification model for recognition, wherein the classification model classifies the other data as first-type data and second-type data,
wherein the anomaly detection model is a first machine learning model and obtained by pre-training based on a first sample data set, and the classification model is a second machine learning model and obtained by pre-training based on a second sample data set different from the first sample data set; and
the pre-training of the classification model comprises:
detecting, by the anomaly detection model, an abnormal sample data set from the second sample data set;
optimizing the abnormal sample data set based on a feature optimization algorithm; and
using the optimized abnormal sample data set to train the classification model.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the pre-training of the anomaly detection model comprises:
acquiring the first sample data set, wherein an amount of first-type data in the first sample data set is greater than that of second-type data; and
using the first sample data set to train the anomaly detection model.

### Claim 3

A risk recognition method for recognizing data as secure data or risky data, wherein the method comprises:
acquiring data to be recognized, and using a preset anomaly detection model to detect whether the data to be recognized is abnormal;
if the data to be recognized is detected not to be abnormal, determining that the data to be recognized is secure data; and
if the data to be recognized is detected to be abnormal, using a preset classification model to recognize that the data to be recognized is secure data or risky data,
wherein the anomaly detection model is a first machine learning model and obtained by pre-training based on a first sample data set, and the classification model is a second machine learning model and obtained by pre-training based on a second sample data set different from the first sample data set; and
the pre-training of the classification model comprises:
detecting, by the anomaly detection model, an abnormal sample data set from the second sample data set;
optimizing the abnormal sample data set based on a feature optimization algorithm; and
using the optimized abnormal sample data set to train the classification model.

### Claim 4 (depends on 3)

The method according to claim 3, wherein the pre-training of the anomaly detection model comprises:
acquiring the first sample data set, wherein an amount of secure data in the first sample data set is greater than that of risky data; and
using the first sample data set to train the anomaly detection model.

### Claim 5

A computer device, comprising:
a processor; and
a memory for storing instructions executable by the processor,
wherein the processor is configured to:
acquire data to be recognized, and use a preset anomaly detection model to detect whether the data to be recognized is first-type data; and
input other data than the first-type data recognized by the anomaly detection model, into a classification model for recognition, wherein the classification model classifies the other data as first-type data and second-type data,
wherein the anomaly detection model is a first machine learning model and obtained by pre-training based on a first sample data set, and the classification model is a second machine learning model and obtained by pre-training based on a second sample data set different from the first sample data set; and
the pre-training of the classification model comprises:
detecting, by the anomaly detection model, an abnormal sample data set from the second sample data set;
optimizing the abnormal sample data set based on a feature optimization algorithm; and
using the optimized abnormal sample data set to train the classification model.

### Claim 6 (depends on 5)

The computer device according to claim 5, wherein the pre-training of the anomaly detection model comprises:
acquiring the first sample data set, wherein an amount of first-type data in the first sample data set is greater than that of second-type data; and
using the first sample data set to train the anomaly detection model.

### Claim 7

A computer device, comprising:
a processor; and
a memory for storing instructions executable by the processor,
wherein the processor is configured to:
acquire data to be recognized, and use a preset anomaly detection model to detect whether the data to be recognized is abnormal data;
if the data to be recognized is detected not to be abnormal, determine that the data to be recognized is secure data; and
if the data to be recognized is detected to be abnormal, use a preset classification model to recognize that the data to be recognized is secure data or risky data,
wherein the anomaly detection model is a first machine learning model and obtained by pre-training based on a first sample data set, and the classification model is a second machine learning model and obtained by pre-training based on a second sample data set different from the first sample data set; and
the pre-training of the classification model comprises:
detecting, by the anomaly detection model, an abnormal sample data set from the second sample data set;
optimizing the abnormal sample data set based on a feature optimization algorithm; and
using the optimized abnormal sample data set to train the classification model.

### Claim 8 (depends on 7)

The computer device according to claim 7, wherein the pre-training of the anomaly detection model comprises:
acquiring the first sample data set, wherein an amount of secure data in the first sample data set is greater than that of risky data; and
using the first sample data set to train the anomaly detection model.

### Claim 9

A non-transitory computer-readable storage medium having stored therein instructions that, when executed by a processor of a computer device, cause the computer device to perform a data type recognition method for recognizing data as first-type data or second-type data, wherein the method comprises:
acquiring data to be recognized, and using a preset anomaly detection model to detect whether the data to be recognized is first-type data; and
inputting other data than the first-type data recognized by the anomaly detection model, into a classification model for recognition, wherein the classification model classifies the other data as first-type data and second-type data,
wherein the anomaly detection model is a first machine learning model and obtained by pre-training based on a first sample data set, and the classification model is a second machine learning model and obtained by pre-training based on a second sample data set different from the first sample data set; and
the pre-training of the classification model comprises:
detecting, by the anomaly detection model, an abnormal sample data set from the second sample data set;
optimizing the abnormal sample data set based on a feature optimization algorithm; and
using the optimized abnormal sample data set to train the classification model.

### Claim 10 (depends on 9)

The non-transitory computer-readable storage medium according to claim 9, wherein the pre-training of the anomaly detection model comprises:
acquiring the first sample data set, wherein an amount of first-type data in the first sample data set is greater than that of second-type data; and
using the first sample data set to train the anomaly detection model.

### Claim 11

A non-transitory computer-readable storage medium having stored therein instructions that, when executed by a processor of a computer device, cause the computer device to perform a risk recognition method for recognizing data as secure data or risky data, wherein the method comprises:
acquiring data to be recognized, and using a preset anomaly detection model to detect whether the data to be recognized is abnormal;
if the data to be recognized is detected not to be abnormal, determining that the data to be recognized is secure data; and
if the data to be recognized is detected to be abnormal, using a preset classification model to recognize that the data to be recognized is secure data or risky data,
wherein the anomaly detection model is a first machine learning model and obtained by pre-training based on a first sample data set, and the classification model is a second machine learning model and obtained by pre-training based on a second sample data set different from the first sample data set; and
the pre-training of the classification model comprises:
detecting, by the anomaly detection model, an abnormal sample data set from the second sample data set;
optimizing the abnormal sample data set based on a feature optimization algorithm; and
using the optimized abnormal sample data set to train the classification model.

### Claim 12 (depends on 11)

The non-transitory computer-readable storage medium according to claim 11, wherein the pre-training of the anomaly detection model comprises:
acquiring the first sample data set, wherein an amount of secure data in the first sample data set is greater than that of risky data; and
using the first sample data set to train the anomaly detection model.