Abstract
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.
Highlights
Mobile message is a way of communication among the people, and billions of mobile device users exchange numerous messages
85% of mails and messages received by mobile users are spam [2]. e cost of mails and messages are very low for senders but high for receipts of these messages. e cost paid some time by service providers and the cost of spam can be measured in the loss of human time and loss of important messages or mails [3]
We proposed a spam detection method using machine learning algorithms such as Logistic regression (LR), k-nearest neighbor, and decision tree for classification of ham and spam messages. e SMS spam collection dataset was considered for testing of the current research. e dataset was divided into two categories: 30% for testing and 70% for training purpose for the predictive models. e evaluation metrics for performance such as specificity, accuracy, and sensitivity were considered evaluating the proposed study. e results obtained from experiments confirmed that the proposed research achieved high accuracy
Summary
Mobile message is a way of communication among the people, and billions of mobile device users exchange numerous messages. In [6], the e-mail classification method was proposed for the detection of spam. E experimental results demonstrated that the bagging ensemble learning approach, using J48 (decision tree) base classifier, performs well than its individual model, and the method achieved high performance in terms of detection accuracy. E proposed technique achieved high performance, and the method effectively detected the spam. In [10], the spam detection method was proposed using machine learning classifiers and 92% accuracy was achieved. We proposed a spam detection method using machine learning algorithms such as LR, k-nearest neighbor, and decision tree for classification of ham and spam messages. K-NN is a classification supervised learning algorithm [18] It predicts the label of class as a fresh input and utilizes the same to its inputs in the training set. E dataset “SMS spam collection dataset” contains 5572 instances and two attributes v1 and v2. e v2 is the input messages which are either spam or nonspam. e predicted
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