Data type recognition, model training and risk recognition methods, apparatuses and devices

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.

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.

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.

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.

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.