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A Framework for SMS Spam and Phishing Detection in Malay Language: a Case Study
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Abstract
Short Message Service (SMS) spam and SMS phishing has been increase nowadays especially in Malay language which is the first language for Malaysia country. Currently, many SMS spam in others language has been proposed, however not yet for Malay language and we are the first to propose these. In addition, this paper also analyst on several frameworks of SMS spam filtering for our SMS spam and phishing detection framework. From the analysis, the chosen framework has been enhanced for Malay SMS spam and phishing. The enhancement has been done on classification phase where our framework proposed dual classification. The classification 1 will classify the SMS into ham and scam SMS. For classification 2, the scam SMS will be classified again into SMS spam and SMS phishing. After dual classifications phase completed, the Malay SMS has been examined using Naïve Bayes and J48 unsupervised Machine Learning techniques. The result shows high accuracy in detecting Malay SMS ham, spam and phishing.
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