Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
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
Classifier fitness based on accuracy
Evolutionary Computation
SMS: The Short Message Service
Computer
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
Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms
IEEE Transactions on Fuzzy Systems
Review: SMS spam filtering: Methods and data
Expert Systems with Applications: An International Journal
Simple SMS spam filtering on independent mobile phone
Security and Communication Networks
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In recent years, we have witnessed the dramatic increase in the volume of mobile SMS (Short Messaging Service) spam. The reason is that operators - owing to fierce market competition - have introduced packages that allow their customers to send unlimited SMS in less than $1 a month. It not only degrades the service of cellular operators but also compromises security and privacy of users. In this paper, we analyze SMS spam to identify novel features that distinguishes it from benign SMS (ham). The novelty of our approach is that we intercept the SMS at the access layer of a mobile phone - in hexadecimal format - and extract two features: (1) octet bigrams, and (2) frequency distribution of octets. Later, we provide these features to a number of evolutionary and non-evolutionary classifiers to identify the best classifier for our mobile spam filtering system. We evaluate the detection rate and false alarm rate of our system - using different classifiers - on a real world dataset. The results of our experiments show that sUpervised Classifier System (UCS), by operating on the the above-mentioned features'set, achieves more than 89% detection rate and 0% false alarm rate.