An abnormal traffic detection method based on neural network

An abnormal traffic and neural network technology, applied in the field of abnormal traffic detection, can solve problems such as uneven data distribution, and achieve the effects of improving detection accuracy, network detection accuracy, and performance.

Active Publication Date: 2022-07-15
HARBIN ENG UNIV
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a method for detecting abnormal traffic based on a neural network that solves the problem of abnormal traffic detection caused by unbalanced data distribution in the training data set and improves the performance of the detection model

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
  • An abnormal traffic detection method based on neural network
  • An abnormal traffic detection method based on neural network
  • An abnormal traffic detection method based on neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0059] 1. Combine figure 1 , the present invention proposes a method for detecting abnormal traffic based on a neural network, which specifically includes the following steps:

[0060] Step 1: Preprocess the original data set, including numericalization and normalization of the data. Numericalization is to represent the discrete character variables with integer data, which is convenient for processing; normalization is to The data of different classes are normalized to between 0-1 to avoid the influence of the large order of magnitude difference.

[0061] Step 2: Augment minority class samples with oversampling method.

[0062] Step 3: Clean the sample using the undersampling method.

[0063] Step 4: Enter the network training to get the model.

[0064] 2. The specific steps for oversampling in the step 2 for constructing minority class samples are:

[0065] Step 2.1: Determine the discrete features dc in the sample, and the number of extensions t for each minority class s...

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 belongs to the technical field of abnormal flow detection, in particular to a method for abnormal flow detection based on a neural network. The present invention firstly resamples traffic samples with unbalanced data through sampling technology to avoid reducing the influence of unbalanced data on detection; then, the sample data is input into the network for detection, and the network is optimized from two aspects of spatial structure and feature weight. , and improve the network structure according to the characteristics of abnormal traffic to improve the accuracy of network detection. The invention combines resampling and neural network to improve the accuracy of the model and effectively improve the effect of abnormal flow detection. The invention can effectively reduce the influence of data imbalance on the classification result, and at the same time optimize the network according to the characteristics of traffic detection, and improve the detection accuracy. The invention solves the abnormal flow detection problem of uneven data distribution in the training data set, and improves the performance of the detection model.

Description

technical field [0001] The invention belongs to the technical field of abnormal flow detection, in particular to a method for abnormal flow detection based on a neural network. Background technique [0002] With the increasing application of Internet technology, security issues have become an urgent security issue to be solved. How to defend against external network attacks has become a key concern of relevant practitioners all over the world. Abnormal traffic detection is an important part of network security defense. In view of the current network security problems in industrial control systems, abnormal traffic detection can be used to collect and detect the traffic in the industrial control network to find possible attacks and make decisions. response. Abnormal traffic detection is to collect the traffic in the network and extract the useful information into features, and use the features to describe the traffic, so as to find out whether there are signs of attacks or v...

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
Patent Type & Authority Patents(China)
IPC IPC(8): H04L9/40G06N3/04G06N3/08
CPCH04L63/1425G06N3/08G06N3/045
Inventor 李明旭
Owner HARBIN ENG UNIV
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