The spam detection is a big issue in mobile message communication due to which mobile message communication is insecure. In order to tackle this problem, an accurate and precise method is needed to detect the spam in mobile message communication. We proposed the applications of the machine learning-based spam detection method for accurate detection. In this technique, machine learning classifiers such as Logistic regression (LR), K-nearest neighbor (K-NN), and decision tree (DT) are used for classification of ham and spam messages in mobile device communication. The SMS spam collection data set is used for testing the method. The dataset is split into two categories for training and testing the research. The results of the experiments demonstrated that the classification performance of LR is high as compared with K-NN and DT, and the LR achieved a high accuracy of 99%. Additionally, the proposed method performance is good as compared with the existing state-of-the-art methods.

中文翻译：

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使用机器学习算法的安全移动消息通信中的垃圾邮件检测方法

由于移动消息通信不安全，垃圾邮件检测是移动消息通信中的一个大问题。为了解决这个问题，需要一种准确而精确的方法来检测移动消息通信中的垃圾邮件。我们提出了基于机器学习的垃圾邮件检测方法在准确检测中的应用。在这项技术中，机器学习分类器（例如Logistic回归（LR），K最近邻（K-NN）和决策树（DT））用于在移动设备通信中对垃圾邮件和垃圾邮件进行分类。SMS垃圾邮件收集数据集用于测试该方法。数据集分为两类，用于训练和测试研究。实验结果表明，与K-NN和DT相比，LR的分类性能高，LR达到了99％的高精度。另外，与现有的最新技术方法相比，所提出的方法性能良好。