Title :
Intelligent spam classification for mobile text message
Author :
Mathew, Kuruvilla ; Issac, Biju
Author_Institution :
Sch. of Eng., Comput. & Sci., Swinburne Univ. of Technol., Kuching, Malaysia
Abstract :
This paper analyses the methods of intelligent spam filtering techniques in the SMS (Short Message Service) text paradigm, in the context of mobile text message spam. The unique characteristics of the SMS contents are indicative of the fact that all approaches may not be equally effective or efficient. This paper compares some of the popular spam filtering techniques on a publically available SMS spam corpus, to identify the methods that work best in the SMS text context. This can give hints on optimized spam detection for mobile text messages.
Keywords :
Bayes methods; electronic messaging; learning (artificial intelligence); mobile communication; pattern classification; text analysis; Bayesian method; SMS spam corpus; intelligent spam classification; intelligent spam filtering; machine learning; mobile text message spam; short message service text paradigm; spam detection; Delta modulation; Education; Filtering; Integrated circuits; Java; Logistics; Unsolicited electronic mail; Bayes Classifier; Intelligent classification; Mobile Spam; SMS spam;
Conference_Titel :
Computer Science and Network Technology (ICCSNT), 2011 International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4577-1586-0
DOI :
10.1109/ICCSNT.2011.6181918
