Home > Archive > 2020 > Volume 10 Number 1 (Jan. 2020) >
IJMLC 2020 Vol.10(1): 182-188 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.1.917

How to Fight against SMS-Spam: Structural Approach and Results

Giseop Noh

Abstract—Although the number of different uses for mobile-data networks has grown rapidly, short message service (SMS) remains the primary message-exchange method; in addition, SMS is still necessary since it provides several advantages including the small monetary cost that is incurred per transmission, greater security compared to online social networks (OSNs). Due to the popular status of SMS, SMS spam is a form of communication that can be used to pursue malicious economic intents such as phishing and illegal advertising, or to widely distribute unwanted messages to numerous phone users. In this paper, we explore the effectiveness of using social structural approach. To this end, we introduce a methodology that shows how to expand SMS networks from small SMS datasets to social networks based on real-world datasets and possible SMS-spam attack. Also, we verify the detection effectiveness of our approach by conducting experiments.

Index Terms—Spam detection, social structural approach, spam attacks

Giseop Noh is with the Cheongju University, Cheongju-si, Chungcheongbuk-do, South Korea, 28503 (e-mail: kafa46@gmail.com).

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Cite: Giseop Noh, "How to Fight against SMS-Spam: Structural Approach and Results," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 182-188, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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