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

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. Henceforth leaving the ESPs faced with a problem of building robust and scalable automatic personalized spam filters. We address this problem 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-spa...

• Type: Conference
• Year: 2007
• Where: WEBI
• Authors: Khurum Nazir Junejo, Asim Karim
• Keywords: Automatic Personalized Spam, Internet Technology, Non-spam E-mails, Spam Filtering, WEBI 2007