Internet of Things intrusion detection method based on machine learning

An intrusion detection and machine learning technology, applied in the field of Internet of Things security, can solve problems such as insufficient computing power, difficulty in meeting demands, and limited power supply energy, and achieve high computing efficiency, low false alarm rate, and high detection rate.

Active Publication Date: 2018-10-26
CHONGQING UNIV OF POSTS & TELECOMM
View PDF4 Cites 28 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, the sensor network of the Internet of Things is composed of a large number of unattended sensor nodes. The traditional intrusion detection system is difficult to meet the demand due to the limited power supply energy of the sensor nodes of the Internet of Things, insufficient computing power, and limited storage space.

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
  • Internet of Things intrusion detection method based on machine learning
  • Internet of Things intrusion detection method based on machine learning
  • Internet of Things intrusion detection method based on machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0048] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0049] Reference figure 1 The detection flowchart shown in the embodiment of the present invention, a method for detecting intrusion of the Internet of Things based on machine learning, includes the following steps:

[0050] 101. In data preprocessing, digitize and standardize the NSL-KDD network intrusion data set;

[0051] 102. Divide the data set and reduce the dimensionality of the data: perform principal component dimensionality reduction on the processed data, so that the relevant feature values ​​of the original data set are transformed into mutually independent or unrelated variables, so that the feature value is reduced and the calculation is reduced the complexity. The data set after dimensionality reduction is divided into training set and test set by cross-validation method.

[0052] 103. Construct a Least Squares Support Vector Mac...

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 Internet of Things intrusion detection method based on machine learning, and belongs to the field of Internet of Things safety. The method comprises the following steps of preprocessing data, dividing a data set and carrying out data dimension reduction, constructing a least squares support vector machine, carrying out sparse processing on the least squares support vector machine, forming a base classifier, constructing a base classifier based on a neural network, carrying out intrusion behavior detection and carrying out prediction experiments. According to the method, the computational complexity is reduced by adopting a least squares support vector machine algorithm, a pruning technology and the like; an improved evolutionary strategy optimization model is adopted to get rid of extreme points, the optimal effect of the model is achieved, and the judgment accuracy can be improved. The method has the characteristics of small computational amount, low false alarm rate and high detection accuracy, and is suitable for intrusion detection in the Internet of Things.

Description

Technical field [0001] The invention belongs to the security field of the Internet of Things, and relates to a method for detecting intrusion of the Internet of Things based on machine learning. Background technique [0002] With the rapid development of Internet of Things technology, Internet of Things products have gradually become popular. However, the current security protection capabilities of smart devices are generally weak. Problems such as incomplete upgrade and maintenance mechanisms and unreasonable smart device security configurations have led to more security in smart devices. Hidden dangers. With the development of the times, a large number of smart devices continue to emerge, but the corresponding security measures are not sound enough. For example, traditional security methods, such as authentication technology to determine its own security, key establishment and distribution mechanisms to ensure secure transmission, and data encryption technology to ensure 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
Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/06H04L29/08G06N3/08G06N3/04G06K9/62
CPCH04L63/1416H04L67/12G06N3/086G06N3/044G06F18/2135G06F18/2453G06F18/2411
Inventor 魏琴芳吕博文胡向东胡蓉李仁杰白银
Owner CHONGQING UNIV OF POSTS & TELECOMM
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