Spam filtering method and system based on deep learning

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 ...

AI Technical Summary

Problems solved by technology

However, the characteristics of spam will continue to change, which requires constant adjustment of the rules, which is undoubtedly very passive and troublesome

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