Abstract
An iPhone is like a portable computer in arithmetic logic capability, memory capacity, and multi-media capability. Many malware targeting iPhone has already emerged and then the situation is getting worse. Accessing e-mails on the internet is not a new capability for iPhone. Other smart phones on the market also provide this capability. The spam e-mails have become a serious problem for iPhone. A novel algorithm, artificial bee-based decision tree (ABBDT) approach, is applied to filter spam e-mails for iPhone in this paper. In the proposed ABBDT approach, decision tree is used to filter spam e-mails for iPhone. In addition, artificial bee algorithm is used to increase the testing accuracy of decision tree. There are total 504 collected e-mails in iPhone dataset and they are categorized into 12 attributes. Another spambase dataset, obtained from UCI repository of machine learning databases, is also used to evaluate the performance of the proposed ABBDT approach. From simulation results, the proposed ABBDT approach outperforms other existing algorithms.
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