Research on spam filtering algorithm based on mutual information and weighted naive Bayesian classification

Abstract

In this paper, N-gram algorithm is used to construct the characteristic database of e-mail viruses. Based on the characteristic database, the transmission and immune automatic behaviour of e-mail viruses are constructed based on the disease transmission model. Based on a large number of samples of five different virus types, a mail virus feature library is generated, and the automatic transmission and immune process of mail virus are analysed according to the mail virus feature library. Then, aiming at the low precision and recall rate of the traditional spam filtering algorithm, an improved mutual information feature and weighted naive Bayesian classification algorithm is proposed to complete the spam filtering. Experiments on trec06c open source data set show that the feature library generated by this method has a good performance of e-mail virus detection, and the analysis of e-mail virus behaviour can better meet the actual work of e-mail virus prevention.