Abnormal alarm identification method and device based on incremental learning, server and storage medium

A technology of incremental learning and recognition methods, applied in devices, abnormal alarm recognition methods based on incremental learning, servers and storage media, can solve the problem that the training data set is not enough to completely describe the characteristics of normal data, and the model is difficult to adapt to the network Problems such as attacks and rule coverage are not new scenarios, etc., to achieve good dynamic scalability and adaptability, improve model performance, and improve accuracy

Inactive Publication Date: 2021-08-13
中国航空油料集团有限公司 +1
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This patented technology allows for better adaptation between different types of threats without requiring significant effort or resources from experts. It involves gradually adding more learnable patterns based upon previous ones, updating them with current knowledge, and adjusting their weights accordingly until they are suitable for future threat scenarios. By doing this, traditional methods like linear regression (LSS) can achieve higher accuracies compared to older techniques such as RandomForest). Overall, this innovative method improves efficiency and effectiveness across various applications while minimizing unnecessary workload.

Problems solved by technology

The technical problem addressed in this patented text relates to improving detecting networks from various types of attacks that may occur frequently or involve highly sensitive systems such as IDC' s servers. Existing methods have limited effectiveness due to their fixed set of rules used during analysis and updating. Additionally, existing approaches cannot efficiently adjust when adding more advanced attacks while maintaining accurate results.

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  • Abnormal alarm identification method and device based on incremental learning, server and storage medium
  • Abnormal alarm identification method and device based on incremental learning, server and storage medium

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Embodiment Construction

[0046] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, various implementation modes of the present invention will be described in detail below in conjunction with the accompanying drawings. However, those of ordinary skill in the art can understand that, in each implementation manner of the present invention, many technical details are provided for readers to better understand the present application. However, even without these technical details and various changes and modifications based on the following implementation modes, the technical solution claimed in this application can also be realized. The division of the following embodiments is for the convenience of description, and should not constitute any limitation to the specific implementation of the present invention, and the various embodiments can be combined and referred to each other on the premise of no contradiction.

[0047] The present inventi

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Abstract

The invention relates to the field of anomaly detection and new high-risk alarm identification by adopting a One Class SVM (Support Vector Machine), and particularly provides an anomaly alarm identification method and device based on incremental learning, a server and a storage medium, the method comprises the following steps: firstly, training a One Class SVM model by utilizing an impure original data set to identify an anomaly alarm; and then, enabling a safety operator to regularly mark abnormal alarms obtained by the model, and iteratively optimize the model based on incremental learning, so that the model detection performance is improved, and missing report and false report are reduced.

Description

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Claims

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Application Information

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Owner 中国航空油料集团有限公司
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