Mobile spam messages have become one of the main concerns in the field of short messaging service (SMS) due to its negative impact on mobile users and networks. The current literature lacks effective solutions for this issue. In this study, the negative impacts of SMS spam were thoroughly analysed, and the existing techniques for SMS spam detection were investigated through two experiments. The first experiment was performed to test and compare the current data mining and cost-sensitive techniques, whereas the second experiment was conducted to test the performance of the proposed technique. Based on the experimental results of the first phase, the most optimal non-cost classifier is a Bayesian network classifier, which is well behaved under the cost-sensitive classifier and obtained the lowest rate of false negative and an acceptable false positive rate. The proposed strategy achieves the best performance in terms of false negative SMS spam classification, obtaining the smallest total expenses and highest precision amongst the compared strategies.

移动垃圾邮件消息由于对移动用户和网络造成负面影响，已成为短消息服务（SMS）领域的主要关注之一。当前的文献对此问题缺乏有效的解决方案。在这项研究中，彻底分析了SMS垃圾邮件的负面影响，并通过两个实验研究了现有的SMS垃圾邮件检测技术。进行第一个实验以测试和比较当前的数据挖掘和成本敏感技术，而第二个实验进行以测试所提出技术的性能。根据第一阶段的实验结果，最佳的非成本分类器是贝叶斯网络分类器，在成本敏感的分类器下表现良好，并获得了最低的误报率和可接受的误报率。所提出的策略在误判SMS垃圾邮件分类方面达到了最佳性能，在比较的策略中获得了最小的总费用和最高的准确性。