An LDoS attack detection method based on multi-feature fusion and a CNN algorithm

A multi-feature fusion and detection method technology, applied in the field of slow denial of service attack detection, can solve the problems of low detection accuracy and poor self-adaptive ability

Inactive Publication Date: 2019-05-07
HUNAN UNIV
View PDF3 Cites 20 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the low detection accuracy and poor adaptive ability of traditional slow denial-

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 LDoS attack detection method based on multi-feature fusion and a CNN algorithm
  • An LDoS attack detection method based on multi-feature fusion and a CNN algorithm
  • An LDoS attack detection method based on multi-feature fusion and a CNN algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0020] The present invention will be further described below in conjunction with the accompanying drawings.

[0021] Such as figure 1 As shown, the slow denial of service attack detection method mainly includes four steps: data sampling, data processing, model training, and judgment detection.

[0022] figure 2 Feature maps generated by feature computation for training data and test data. This process includes two steps, specifically: 1) within the unit time, using the data slice as the feature calculation unit, to obtain the feature matrix of the network data within the unit time; 2) through numerical conversion mapping, the feature matrix is ​​transformed into a feature matrix picture. When an attack occurs on a network, many characteristics will change. Therefore, there will be large differences in the feature matrix obtained through feature calculation. Thus, the feature maps generated by numerical transformation will be different. This is also the reason why the CN...

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 a slow denial of service (LDoS) attack detection method based on multi-feature fusion and a convolutional neural network (CNN) algorithm, and belongs to the field of network security. The method comprises the following steps: obtaining related data messages in a network key routing node in unit time to form a training sample and a test sample; Performing feature calculationon the training sample and the test sample, and generating a corresponding feature map; Using the feature map of the training sample to train a CNN model, enabling the CNN model to learn and memorizethe features of the slow denial of service attack, and finally obtaining a model which can be used for detecting the slow denial of service attack; And detecting the feature map of the test sample byusing the trained CNN model, and judging whether a slow denial of service attack occurs in a unit time corresponding to the feature map according to a judgment criterion. The detection method based on multi-feature fusion and the CNN algorithm provided by the invention can detect the slow denial of service attack in the network in a high-precision and self-adaptive manner.

Description

technical field [0001] The invention belongs to the field of computer network security, in particular to a slow denial of service (LDoS) attack detection method based on multi-feature fusion and convolutional neural network (CNN) algorithm. Background technique [0002] Denial of service (DoS) attack has developed to the present, and its forms are ever-changing. Usually, any attack method that can make the server unable to provide normal services or reduce the performance of the server through legal means belongs to the category of denial of service attacks, and the object of the attack can be any networked computer, router or the entire network. The slow denial of service attack is one of the variants, which is more difficult to detect and more threatening than the traditional denial of service attack. [0003] So far, although many people have proposed many methods, there is still no mature solution. At present, there are two problems in the detection of slow denial of s...

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): H04L29/06G06K9/62
Inventor 汤澹唐柳冯叶詹思佳施玮满坚平陈静文罗能光
Owner HUNAN 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