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

Authors: Xincun Yang; Haiyun Yu; ZhiYi Jia

Addresses: Qinghai University, Qinghai Xining 810016, China ' Qinghai University, Qinghai Xining 810016, China ' Qinghai University, Qinghai Xining 810016, China

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.

Keywords: mail virus feature library; spam automatic behaviour; mutual information feature; weighted naive Bayes; spam filtering.

DOI: 10.1504/IJAHUC.2021.117313

International Journal of Ad Hoc and Ubiquitous Computing, 2021 Vol.37 No.4, pp.240 - 248

Received: 20 Oct 2020
Accepted: 29 Jan 2021
Published online: 31 Aug 2021 *

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