Elements of information theory
Elements of information theory
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
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
cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Proceedings of the 2nd ACM workshop on Security and artificial intelligence
Developing an immunity to spam
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
An AIS-based e-mail classification method
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
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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.