Bw-the Lvq Mail Filtering Model

As the popularization of Internet, e-mails are more and more frequently used,benefiting from its high efficiency, convenience and low cost. At the same time,however, their byproduct, spams are bringing endless trouble to Internet users,network administrators and Internet service providers. With the spreading of thesecarriers for bad or useless information, the users'time is wasted, the bandwidth andstorage space are consumed, and even the Internet is congested. The mature spamfiltering methods used now combine both the automatic filtering of the software andmanual management, which has been proved not adaptive to variety of spams, and itis estimated only 50% of spams can be detected. Therefore, more intelligent filteringtechniques are required.This main goal of this paper is to explore a specific spam filtering model,implement and test it. During our research, we need to examine carefully whether themodel is a fit one, and observe how the parameters of this model itself and theenvironmental parameters influence the filtering performance. So, the test shouldreveal the feasibility and efficiency of this model thoroughly. The author has achievedthe goal above.This paper put forwards two filtering model, Learning VectorQuantization(LVQ)and improved Black&White List(BW), described the designprinciples, discussed their inter-relationship and their relationship with the mail server,and provided important implementation framework and codes. LVQ model solved thediscretion of eigenitem in the Boolean filtering model and the difficulties indistinguishing between spams and normal mails. Improved BW model madeimprovements over the traditional black list and white list model, and decreased theusers'loss due to incorrect bordering address.Though varieties of filtering methods exist now, a large number of problemsabout spam filtering still remain to be solved, which impedes the filteringperformance from improving. New filtering models brought forward in this paper hasprovided some solutions. It has been proved that they can improve the filteringperformance in some environment. Therefore, the research is of great value.