Biblio

Text messaging is used by more people around the world than any other communications technology. As such, it presents a desirable medium for spammers. While this problem has been studied by many researchers over the years, the recent increase in legitimate bulk traffic (e.g., account verification, 2FA, etc.) has dramatically changed the mix of traffic seen in this space, reducing the effectiveness of previous spam classification efforts. This paper demonstrates the performance degradation of those detectors when used on a large-scale corpus of text messages containing both bulk and spam messages. Against our labeled dataset of text messages collected over 14 months, the precision and recall of past classifiers fall to 23.8% and 61.3% respectively. However, using our classification techniques and labeled clusters, precision and recall rise to 100% and 96.8%. We not only show that our collected dataset helps to correct many of the overtraining errors seen in previous studies, but also present insights into a number of current SMS spam campaigns.