Application Of Evolutionary Algorithms In Detecting Sms Spam At Access Layer

In recent years, Short Message Service (SMS) has been widely exploited in arbitrary advertising campaigns and the propagation of scam. In this paper, we first analyze the role of SMS spam as an increasing threat to mobile and smart phone users. Afterward, we present a filtering method for controlling SMS spam on the access layer of mobile devices. We analyze the role of different evolutionary and non-evolutionary classifiers for our spam filter by assimilating the byte-level features of SMS. We evaluated our framework on real-world benign and spam datasets collected from Grumbletext and the users in our social networking community. The results of carefully designed experiments demonstrated that the evolutionary classifiers, like the Structural Learning Algorithm in Vague Environment (SLAVE), could efficiently detect spam messages at the access layer of a mobile device. To the best of our knowledge, the current work is the first SMS spam filter based on evolutionary classifier that works on the access layer of a mobile device. The results of our experiments show that our framework, using evolutionary algorithms, achieves a detection accuracy of more than 93%, with false alarm rate of <= 0.13% in classifying spam SMS. Moreover, the memory requirement for incorporating SMS features is relatively small, and it takes less than one second to classify a message as spam or benign.