NDLI: PSSF: A Novel Statistical Approach for Personalized Service-side Spam Filtering

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2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology - Cover

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PSSF: A Novel Statistical Approach for Personalized Service-side Spam Filtering

Content Provider	ACM Digital Library
Author	Karim, Asim Junejo, Khurum Nazir
Abstract	The volume of spam e-mails has grown rapidly in the last two years resulting in increasing costs to users, network operators, and e-mail service providers (ESPs). E-mail users demand accurate spam filtering with minimum effort from their side. Since the distribution of spam and non-spam e-mails is often different for different users a single filter trained on a general corpus is not optimal for all users. The question asked by ESPs is: How do you build robust and scalable automatic personalized spam filters? We address this question by presenting PSSF, a novel statistical approach for personalized service-side spam filtering. PSSF builds a discriminative classifier from a statistical model of spam and non-spam e-mails. A classifier is first built on a general training corpus that is then adapted in one or more passes of soft labeling and classifier rebuilding over each user's unlabeled e-mails. The statistical model captures the distribution of tokens in spam and non-spam e-mails. This model is robust in the sense that its size can be reduced significantly without degrading filtering performance. We evaluate PSSF on two datasets. The results demonstrate the superior performance and scalability of PSSF in comparison with other published results on the same datasets.
Starting Page	228
Ending Page	234
Page Count	7
File Format	PDF
ISBN	0769530265
DOI	10.1109/WI.2007.86
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-11-02
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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