S. Dixit, S. Gupta, and C.V. Ravishankar (USA)
Spam, short text message (SMS), SMSC, mobile, Clustering
The efficient and accurate control of spams on mobile handsets is an important problem. Mobile spam incurs a cost on a per-message basis, degrades normal cellular service, and is a nuisance and breach of privacy. It is also a popular enabler of mobile fraud. In countries such as South Korea and Japan, Mobile Spamming generates almost half of the total SMS traffic. In this paper we propose a novel spam control technique based on random projections, designed to run on SS7 links so that spams are supressed before they reach users. Our is a non Bayesian, non–keyword approach, which rate limits candidate spam messages to foil spammers. We demonstrate using real-world spam messages that random projection is a robust, efficient and accurate method to identify SMS spam. We give a mathematical formulation of the SMS spam problem and demonstrate that it models the real world short message spam paradigm accurately. Based on this formulation, we describe a framework and algorithm to efficiently identify and control spam messages at the SMSC switch of a mobile network.
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