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

Document Type : Original Article

Authors

Department of Computer, Gorgan Branch, Islamic Azad University, Gorgan, Iran

Abstract

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 real-world 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.
Moreover, the achieved results were compared against three recently published works. The results show that the proposed approach achieved high detection rate, which was successful in terms of the F-measure and classification accuracy compared with other considered researches.

Keywords

References

 
1. Cormack, G.V., "Email spam filtering: A systematic review",
Foundations and Trends® in Information Retrieval,  Vol. 1,
No. 4, (2008), 335-455. 
2. Almeida, T.A., Hidalgo, J.M.G. and Yamakami, A.,
"Contributions to the study of sms spam filtering: New
collection and results", in Proceedings of the 11th ACM
symposium on Document engineering., (2011), 259-262. 
3. Parandeh Motlagh, F. and Khatibi Bardsiri, A., "Detecting fake
websites using swarm intelligence mechanism in human
learning", International Journal of Engineering, Transactions
A: Basics, Vol. 31, No. 10, (2018), 1642-1650. 
4. Mohammadi, A. and Hamidi, H., "Analysis and evaluation of
privacy protection behavior and information disclosure concerns
in online social networks", International Journal of
Engineering, Transactions B: Applications, Vol. 31, No. 8,
(2018), 1234-1239. 
5. Jain, A.K. and Gupta, B.B., "Phishing detection: Analysis of
visual similarity based approaches", Security and
Communication Networks,  Vol. 2017, No., (2017). 
6. Yamakami, T., "Impact from mobile spam mail on mobile
internet services", in International Symposium on Parallel and Distributed Processing and Applications, Springer., (2003), 179184.
7. Gupta, B.B., Tewari, A., Jain, A.K. and Agrawal, D.P.,
"Fighting against phishing attacks: State of the art and future
challenges", Neural Computing and Applications,  Vol. 28, No.
12, (2017), 3629-3654. 
8. Choudhary, N. and Jain, A.K., "Comparative analysis of mobile
phishing detection and prevention approaches", in International
Conference on Information and Communication Technology for
Intelligent Systems, Springer., (2017), 349-356. 
9. Puniškis, D., Laurutis, R. and Dirmeikis, R., "An artificial neural
nets for spam e-mail recognition", Elektronika ir
Elektrotechnika,  Vol. 69, No. 5, (2006), 73-76. 
10. Ji, H. and Zhang, H., "Analysis on the content features and their
correlation of web pages for spam detection", China
Communications,  Vol. 12, No. 3, (2015), 84-94. 
11. Kim, S.-E., Jo, J.-T. and Choi, S.-H., "Sms spam filterinig using
keyword frequency ratio", International Journal of Security
and Its Applications,  Vol. 9, No. 1, (2015), 329-336. 
12. Zainal, K., Sulaiman, N. and Jali, M., "An analysis of various
algorithms for text spam classification and clustering using
rapidminer and weka", International Journal of Computer
Science and Information Security,  Vol. 13, No. 3, (2015), 66. 
13. El-Alfy, E.-S.M. and AlHasan, A.A., "Spam filtering framework
for multimodal mobile communication based on dendritic cell
algorithm", Future Generation Computer Systems,  Vol. 64,
(2016), 98-107. 
14. Taufiq Nuruzzaman, M., Lee, C., Abdullah, M.F.A.b. and Choi,
D., "Simple sms spam filtering on independent mobile phone",
Security and Communication Networks,  Vol. 5, No. 10,
(2012), 1209-1220. 
15. Chan, P.P., Yang, C., Yeung, D.S. and Ng, W.W., "Spam 
 
 
 
filtering for short messages in adversarial environment",
Neurocomputing,  Vol. 155, (2015), 167-176. 
16. Uysal, A.K., Gunal, S., Ergin, S. and Gunal, E.S., "A novel
framework for sms spam filtering", in 2012 International
Symposium on Innovations in Intelligent Systems and
Applications, IEEE., (2012), 1-4. 
17. Serrano, J.M.B., Palancar, J.H. and Cumplido, R., "The
evaluation of ordered features for sms spam filtering", in
Iberoamerican Congress on Pattern Recognition, Springer.,
(2014), 383-390. 
18. Junaid, M.B. and Farooq, M., "Using evolutionary learning
classifiers to do mobilespam (SMS) filtering", in Proceedings of
the 13th annual conference on Genetic and evolutionary
computation., (2011), 1795-1802. 
19. Gómez Hidalgo, J.M., Bringas, G.C., Sánz, E.P. and García,
F.C., "Content based sms spam filtering", in Proceedings of the
2006 ACM symposium on Document engineering., (2006), 107114.
20. Choudhary, N. and Jain, A.K., "Towards filtering of sms spam
messages using machine learning based technique", in
International Conference on Advanced Informatics for
Computing Research, Springer., (2017), 18-30. 
21. Suleiman, D. and Al-Naymat, G., "Sms spam detection using
h2o framework", Procedia Computer Science,  Vol. 113,
(2017), 154-161. 
22. Dua, D. and Graff, C., Uci machine learning repository. 2017.
23. Gholami, M., "Islanding detection method of distributed 
generation based on wavenet", International Journal of
Engineering,  Transactions B: Applications, Vol. 32, No. 2,
(2019), 242-248. 
24. Gharvirian, F. and Bohloli, A., "Neural network based protection
of software defined network controller against distributed denial
of service attacks", International Journal of Engineering,
Transactions B: Applications, Vol. 30, No. 11, (2017), 17141722.
 
 
International Journal of Engineering
Volume 33, Issue 2
TRANSACTIONS B: Applications
February 2020
Pages 221-228

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