Content based SMS spam filtering
Proceedings of the 2006 ACM symposium on Document engineering
Feature engineering for mobile (SMS) spam filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Spam filtering for short messages
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
The contribution of stylistic information to content-based mobile spam filtering
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Teaching Johnny not to fall for phish
ACM Transactions on Internet Technology (TOIT)
Challenges and novelties while using mobile phones as ICT devices for Indian masses: short paper
Proceedings of the 4th ACM Workshop on Networked Systems for Developing Regions
Mobile computing: the next decade
ACM SIGMOBILE Mobile Computing and Communications Review
Phi.sh/$oCiaL: the phishing landscape through short URLs
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
Review: SMS spam filtering: Methods and data
Expert Systems with Applications: An International Journal
Short paper: enhancing users' comprehension of android permissions
Proceedings of the second ACM workshop on Security and privacy in smartphones and mobile devices
Simple SMS spam filtering on independent mobile phone
Security and Communication Networks
The curse of 140 characters: evaluating the efficacy of SMS spam detection on android
Proceedings of the Third ACM workshop on Security and privacy in smartphones & mobile devices
SEC'13 Proceedings of the 22nd USENIX conference on Security
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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. We 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 on a India centric dataset. In our ongoing research, as an exploratory step, we have developed a mobile-based system SMSAssassin that can filter SMS spam messages based on bayesian learning and sender blacklisting mechanism. Since the spam SMS keywords and patterns keep on changing, SMSAssassin uses crowd sourcing to keep itself updated. Using a dataset that we are collecting from users in the real-world, we evaluated our approaches and found some interesting results.