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S-Detector: an enhanced security model for detecting Smishing attack for mobile computing

Author

Listed:
  • Jae Woong Joo

    (Seoul National University of Science and Technology (SeoulTech))

  • Seo Yeon Moon

    (Seoul National University of Science and Technology (SeoulTech))

  • Saurabh Singh

    (Seoul National University of Science and Technology (SeoulTech))

  • Jong Hyuk Park

    (Seoul National University of Science and Technology (SeoulTech))

Abstract

Recently the mobile computing technology has been generally used to people with the development of the IT technology. The mobile computing environment has provided a convenient environment via the intelligent devices such as tablet PC, smartphone, etc. However, many security threats in the mobile computing environment exist. Therefore, secure elements to protect against security threats are needed. In particular, Short Message Service phishing (Smishing) damage has continued to increase with the normalization of mobile computing environment. We discuss and analyze the security considerations about Smishing in mobile computing environments. In addition, we propose an enhanced security model for detecting Smishing attack (we called “S-Detector”). The proposed model is applied to a Naive Bayes classifier to improve the Smishing attack detection in smart devices. This model distinguishes normal text message and Smishing message. And this is mainly used to filter by using statistical learning method. As a result, it is possible to analyze a text message and effectively detect SMS phishing. Finally, we demonstrate the efficiency of our model through the evaluation and analysis of our proposed model.

Suggested Citation

  • Jae Woong Joo & Seo Yeon Moon & Saurabh Singh & Jong Hyuk Park, 2017. "S-Detector: an enhanced security model for detecting Smishing attack for mobile computing," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 66(1), pages 29-38, September.
    • Handle: RePEc:spr:telsys:v:66:y:2017:i:1:d:10.1007_s11235-016-0269-9
    DOI: 10.1007/s11235-016-0269-9
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    Cited by:

    1. Elif Ustundag Soykan & Mustafa Bagriyanik, 2020. "The Effect of SMiShing Attack on Security of Demand Response Programs," Energies, MDPI, vol. 13(17), pages 1-17, September.
    2. Abdallah Ghourabi & Mahmood A. Mahmood & Qusay M. Alzubi, 2020. "A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages," Future Internet, MDPI, vol. 12(9), pages 1-16, September.

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