Spam mail filtering system and method based on clusters

Abstract

The invention discloses a spam mail filtering system and method based on clusters. The system comprises a cluster module, a mail training module, a characteristic base storage module and a mail filtering module. The method includes that S1.1 training mails and unmarked mails are obtained from a mail backup system; S1.2 a cluster preprocessing module preprocesses the mails; S1.3 a cluster analyzing module divides the preprocessed mails into different clusters; S1.4 a cluster central computing module computes vector expression of the clusters; S1.5 the training module studies marked mails in the clusters and updates characteristic bases corresponding to the clusters; S2.1 mails required to be filtered are obtained from a mail system; S2.2 a mail class attribute distinguishing module calculates the cluster most similar to mail content; S2.3 a mail characteristic extraction module conducts characteristic extraction on the mails to be distinguished; S2.4 a mail distinguishing module shows a distinguishing result according to the mail characteristics and the corresponding characteristic bases. The system and method has the advantages of being high in extraction speed, high in accuracy and good in effect.

Description

technical field [0001] The invention relates to the technical field of spam filtering, in particular to a cluster-based spam filtering system and method. Background technique [0002] With the popularity of e-mail, spammers send a large amount of spam to the network through very cheap means, which seriously affects the bandwidth of the network, interferes with the normal use of users and poses a potential threat to user security. [0003] At present, most email filtering systems based on machine learning only use a specific learning algorithm to conduct a single training study on the training email set, and then identify new emails based on the feature library generated by the learning algorithm modeling. This kind of uniform training of training data without distinction, as a method commonly used by machine learning algorithms in the field of mail filtering, can provide filters with better learning ability, but because of the lack of differentiated treatment of training dat...

AI Technical Summary

Problems solved by technology

This kind of uniform training of training data without distinction, as a method commonly used by machine learning algorithms in the field of mail filtering, can provide filters with better learning ability, but because of the lack of differentiated treatment of training data, in The learning and discriminative ability of the filter will be affected

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