Manufacturing industry-oriented field process behavior abnormal behavior detection method

Abstract

The invention relates to a manufacturing industry field process abnormal behavior detection method, which is mainly used for researching a detection method based on a behavior characteristic knowledgebase for manufacturing industry field process abnormal behavior detection, and is used for constructing a random forest abnormal detection model based on a random forest algorithm. According to the specific implementation method, PCA method dimension reduction is carried out on process data of an industrial field, feature selection is carried out by adopting an integrated rule tree model, and theprocess data is classified by adopting a random forest algorithm. Each path of the random forest corresponds to one rule, good interpretability is achieved, the classification accuracy is greatly improved, a large number of input variables can be processed, and even if process data contains missing values, the classification result can still reach high accuracy. A behavior characteristic knowledge base and a real-time emergency decision-making framework of an unknown behavior scene are fused, and abnormal behaviors are early warned in advance by predicting behavior detection of industrial field process data.

Description

technical field [0001] The invention relates to an abnormal behavior detection method for on-site process behavior in the manufacturing industry. By constructing a knowledge base based on industrial on-site behavior characteristics, the correlation between industrial control safety detection technology and on-site behavior in the industry is realized, and the abnormal behavior detection alarm is more representative of the industry. On-site behavior characteristics belong to the field of industrial control network information security. Background technique [0002] At present, the research on abnormal behavior detection technology based on industry characteristic knowledge base is relatively blank at home and abroad. At present, industrial control protocols are added on the basis of traditional IT security protection products. Now the technology used for abnormal detection is based on support vector machines. Anomaly detection, its data space and time costs are relatively lar...

AI Technical Summary

Problems solved by technology

[0002] At present, the research on abnormal behavior detection technology based on industry characteristic knowledge base is relatively blank at home and abroad. At present, industrial control protocols are added on the basis of traditional IT security protection products. Now the technology used for abnormal detection is based on support vector machines. Anomaly detection, its data space and time costs are relatively large, and requires a large amount of storage space. Anomaly detection based on the naive Bayesian method, when the number of features is large and the correlation between features is large, the classification effect is not good

The prediction result of anomaly detection based on decision tree algorithm is unstable and the variance is large

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