Using Evolutionary Learning Classifiers to Do Mobile Spam (SMS) Filtering
In recent years, the authors 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, they analyze SMS spam to identify novel features that distinguishes it from benign SMS (ham). The novelty of their approach is that they intercept the SMS at the access layer of a mobile phone - in hexadecimal format - and extract two features: octet bigrams, and frequency distribution of octets.