The Evaluation of Ordered Features for SMS Spam Filtering

Serrano, José Manuel Bande; Palancar, José Hernández; Cumplido, René

doi:10.1007/978-3-319-12568-8_47

Cited by 6 publications

(1 citation statement)

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“…Serrano et al [17] presented a new method for SMS spam filtering based on writing style using extrinsic information. In their research, they saved spam and ham writing styles using sequential labeling and term clustering extraction techniques.…”

Section: Related Workmentioning

confidence: 99%

An Effective Model for SMS Spam Detection Using Content-based Features and Averaged Neural Network

Sheikhi

Kheirabadi

Bazzazi

2020

IJE

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In recent years, there has been considerable interest among people to use short message service (SMS) as one of the essential and straightforward communications services on mobile devices. The increased popularity of this service also increased the number of mobile devices attacks such as SMS spam messages. SMS spam messages constitute a real problem to mobile subscribers; this worries telecommunication service providers as it disturbs their customers and causes them to lose business. Therefore, in this paper, we proposed a novel machine learning method for detection of SMS spam messages. The proposed model contains two main stages: feature extraction and decision making. In the first stage, we have extracted relevant features from the dataset based on the characteristics of spam and legitimate messages to reduce the complexity and improve performance of the model. Then, an averaged neural network model was applied on extracted features to classify messages into either spam or legitimate classes. The method is evaluated in terms of accuracy and F-measure metrics on a realworld SMS dataset with over 5000 messages. Moreover, the achieved results were compared against three recently published works. Our results show that the proposed approach achieved successfully high detection rates in terms of F-measure and classification accuracy, compared with other considered researches.

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Section: Related Workmentioning

confidence: 99%