Incremental learning-based traffic exception detection method and device, and storage medium

A technology of incremental learning and flow detection, which is applied in the direction of instruments, character and pattern recognition, digital transmission systems, etc., can solve problems such as time lag, poor real-time performance, and failure, and achieve increased diversity, accurate prediction, and generalization capabilities enhanced effect

Inactive Publication Date: 2018-06-15
BEIJING TOPSEC NETWORK SECURITY TECH +2
View PDF6 Cites 37 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] 1. The real-time performance is poor. Whether it is based on the selection of statistical analysis threshold or the construction of machine learning-based models, it is necessary to analyze the offline data first, and then deploy the production environment online, and the traffic data is hourly. It is constantly changi

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Incremental learning-based traffic exception detection method and device, and storage medium
  • Incremental learning-based traffic exception detection method and device, and storage medium
  • Incremental learning-based traffic exception detection method and device, and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0045] see figure 1 As shown, in order to solve the problems in the prior art, the present invention provides a method for detecting abnormal traffic based on incremental learning,

[0046] Step S100: Acquiring traffic data of the client;

[0047] Step S200: Using the pre-built traffic detection classifier in the abnormality detection device to perform abnormality detection on the traffic data;

[0048] Step S300: When abnormal data is detected, training sam...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an incremental learning-based traffic exception detection method and device, and a storage medium. The method comprises the steps of acquiring traffic data of a user terminal;detecting exception of the traffic data by using a pre-created traffic detection classifier in an exception detection device; and when abnormal data is detected, obtaining training sample data based on the abnormal data, and performing online training on the traffic detection classifier by using the training sample data. The generalization capability of the classifier is improved by diversifying the training samples.

Description

technical field [0001] The invention relates to a network traffic abnormality detection technology, in particular to an abnormal traffic detection method, device and storage medium based on incremental learning. Background technique [0002] Abnormal network traffic refers to the situation where network traffic deviates from its normal track, such as: operation behaviors that occupy resources, aggressive behaviors, etc., especially the abnormalities generated by attack behaviors will threaten the security of the entire network. The purpose of traffic anomaly detection is to discover these anomalies in time and make quick responses. [0003] Current traffic anomaly detection methods include detection methods based on statistical analysis and detection methods based on machine learning. [0004] Based on the method of statistical analysis, data flow sampling can be analyzed according to time series, statistical analysis can be carried out from multiple dimensions such as data...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): H04L12/26H04L29/06G06K9/62
CPCH04L43/0823H04L63/1425G06F18/24G06F18/214
Inventor 薛智慧潘季明贾蓉高宏建
Owner BEIJING TOPSEC NETWORK SECURITY TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap