Spam filtering method and system based on deep learning

A spam filtering and deep learning technology, applied in the field of spam filtering, can solve problems such as troubles, improve accuracy and stability, save time and manpower

Inactive Publication Date: 2016-11-09
KONKA GROUP
View PDF4 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the characteristics of spam will continue to change, which requires con

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
  • Spam filtering method and system based on deep learning
  • Spam filtering method and system based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0054] The present invention provides a method and system for filtering spam based on deep learning. In order to make the objectives, technical solutions and effects of the present invention clearer and clearer, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0055] The present invention provides a method for filtering spam based on deep learning. Through the self-learning ability of the deep belief network, combined with the advantages of big data, the large number of samples existing on the network are used to learn to improve the classification ability. On the one hand, it can improve the identification of spam On the other hand, the deep belief network is a semi-supervised learning model, which can be trained with a large-scale non-standard sample set....

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 spam filtering method and system based on deep learning. The spam filtering method based on deep learning comprises the first step of processing mail samples to generate a first vector space model and establishing a deep belief network, the second step of processing test mails to generate a second vector space model, the third step of detecting the second vector space model through the established deep belief network, and the fourth step of outputting a detection result. According to the spam filtering method based on deep learning, as the mode that the deep belief network is established and the test mails are detected through the established belief network is adopted, the accuracy and stability of spam recognition are improved, and the time and labor for labeling a large number of samples are saved.

Description

technical field [0001] The present invention relates to the technical field of spam filtering, in particular to a spam filtering method and system based on deep learning. Background technique [0002] With the rapid development of Internet technology, e-mail has become an indispensable part of people's life, work and study. It provides great convenience for our life, but the corresponding spam is causing more and more troubles to people's life. [0003] The core problem of email filtering is how to use known email text data sets to build a text classification model, and then use this model to distinguish email types, thereby filtering out spam. At present, the following algorithms are commonly used, such as: K-Nearest Neighbor Algorithm (KNN), Naive Bayesian Algorithm, Decision Tree Algorithm, Support Vector Machine Algorithm. But these algorithms have their own limitations. [0004] Naive Bayesian algorithm, no matter how the probability model is selected, the model can ...

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): G06F17/30
CPCG06F16/9535
Inventor 杨卫国邹伟何震宇
Owner KONKA GROUP
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