SMSAssassin: Crowdsourcing Driven Mobile-Based System for SMS Spam Filtering

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Provided by: Association for Computing Machinery
Topic: Security
Format: PDF
Due to increase in use of Short Message Service (SMS) over mobile phones in developing countries, there has been a burst of spam SMSes. Content-based machine learning approaches were effective in filtering email spams. Researchers have used topical and stylistic features of the SMS to classify spam and ham. SMS spam filtering can be largely influenced by the presence of regional words, abbreviations and idioms. The authors have tested the feasibility of applying bayesian learning and Support Vector Machine (SVM) based machine learning techniques which were reported to be most effective in email spam filtering.
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