Graph-based learning model for detection of SMS spam on smart phones.

Short Message Service (SMS) has been increasingly exploited through spam propagation schemes in recent years. This paper presents a new method for graph-based learning and classification of spam SMS on mobile devices and smart phones. Our approach is based an modeling the content and patterns of SMS syntax into a directed-weighted graph through exploiting modem composition style of messages. The graph attributes are then used to classify spam messages in real-time by using KL-Divergence measure. Experimental results on two real-world datasets show that our proposed method achieves high detection accuracy with less false alann rate to detect spam messages. Moreover, our approach requires relatively less memory and processing power, making it suitable to deploy on resource-constrained mobile devices and smart phones.