Title
Collaborative spam filtering based on incremental ontology learning
Abstract
Spam mail filtering is a classic problem to automatically recognize irrelevance between incoming emails and user contexts. This paper proposes a novel proxy server architecture for (i) collaboratively integrating useful features sent from personal email clients. (ii) Improving the filtering performance of SMTP servers. Given a set of spam mails marked by multiple email users, the proxy server can extract two kinds of textual features, which are apriori terms and concept terms based on key phrases. More importantly, by taking into account the semantics and statistical associations, the proxy can aggregate them in a hierarchical cluster structure. As a result, spam ontology can be built, and also, incrementally enriched. Hence, the email clients can be supported to improve their performances of spam filtering by referring to the semantic information from the ontology. For evaluating the proposed system, we have collected a large number of spam mails within a same intranet environment. The system has shown 17.4% lower error rate of filtering than the single email clients.
Year
DOI
Venue
2013
10.1007/s11235-011-9513-5
Telecommunication Systems
Keywords
Field
DocType
Intelligent intranet, Spam filtering, Ontology learning, Proxy server
Bag-of-words model,Ontology,World Wide Web,HTML email,Computer science,Server,Spam and Open Relay Blocking System,Forum spam,Ontology learning,Proxy server
Journal
Volume
Issue
ISSN
52
2
1572-9451
Citations 
PageRank 
References 
1
0.35
27
Authors
4
Name
Order
Citations
PageRank
Xuan Hau Pham1536.75
Namhee Lee2224.01
Jason J. Jung31451135.51
Abolghasem Sadeghi-Niaraki4296.53