Indexed by：
Abstract：
With the rapid development of mobile SMS (short message service), spam messages have grown explosively which trouble our daily lives seriously and lead to the loss of telecom operators. In this paper, an online spam filter based on the analysis of two criteria of content representations and relationship between the senders and receivers in social network is proposed. A Naive Bayesian classifier is used to build up the filter including both the content features and social network features. We use the data provided by a partner telecom operator to do the experiments. The results show that our model is effective and satisfies all the requirements of our partner and will be deployed recently. © 2012 IEEE.
Keyword：
Reprint 's Address：
Email：
Version：
Source ：
Year： 2012
Page： 444-449
Language： English
Cited Count：
SCOPUS Cited Count： 9
ESI Highly Cited Papers on the List： 0 Unfold All
WanFang Cited Count：
Chinese Cited Count：
30 Days PV： 1
Affiliated Colleges：
