Abstract
Increase in the popularity and reach of short text messages has led to their usage in propagating unsolicited advertising, promotional offers, and other unwarranted material to users. This has led to a high influx of such spam messages. In order to protect the interests of the user, several countermeasures have been deployed by telecommunication companies to hinder the volume of such spam. However, some volume of spam messages still manage to avoid these measures and cause varying degree of annoyance to users. In this chapter, an automated spam detection algorithm is proposed to deal with the particular problem of short text message spam. The proposed algorithm performs the two class (spam, ham) classification using stylistic and text features specific to short text messages. The algorithm is evaluated on three databases belonging to diverse demographic settings. Experimental results indicate that the proposed algorithm is highly accurate in detecting spam in short messages and can be utilized by a wide variety of users to reduce the volume of spam messages.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Almeida, T.A., Hidalgo, J.M.G., Yamakami, A.: Contributions to the study of sms spam filtering: new collection and results. In: ACM Symposium on Document Engineering, pp. 259–262 (2011)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3) (2011)
Cormack, G.V., Gómez Hidalgo, J.M., Sánz, E.P.: Spam filtering for short messages. In: Conference on Information and Knowledge Management, pp. 313–320 (2007)
Delany, S.J., Zamolotskikh, A.: An assessment of case base reasoning for short text message classification (2004)
Deng, W.W., Peng, H.: Research on a naive bayesian based short message filtering system. In: International Conference on Machine learning and cybernetics, pp. 1233–1237 (2006)
Gómez Hidalgo, J.M., Bringas, G.C., Sánz, E.P., GarcÃa, F.C.: Content based sms spam filtering. In: ACM Symposium on Document Engineering, pp. 107–114 (2006)
Jiang, N., Jin, Y., Skudlark, A., Zhang, Z.L.: Understanding sms spam in a large cellular network: characteristics, strategies and defenses. Res. Attacks Intrusions Defenses 8145, 328–347 (2013)
Junaid, M.B., Farooq, M.: Using evolutionary learning classifiers to do mobilespam (sms) filtering. In: Genetic and Evolutionary Computation Conference, pp. 1795–1802 (2011)
Liu, W., Wang, T.: Index-based online text classification for sms spam filtering. J. Comput. 5(6), 844–851 (2010)
Longzhen, D., An, L., Longjun, H.: A new spam short message classification. Int. Workshop Educ. Technol. Comput. Sci. 2, 168–171 (2009)
Murynets, I., Piqueras Jover, R.: Crime scene investigation: sms spam data analysis. In: ACM Conference on Internet Measurement Conference, pp. 441–452 (2012)
Narayan, A., Saxena, P.: The curse of 140 characters: evaluating the efficacy of sms spam detection on android. In: Third ACM Workshop on Security and Privacy in Smartphones and Mobile Devices, pp. 33–42 (2013)
Qian, X., Evan, W.X., Yang, Q.: Sms spam detection using non-content features (2012)
Rafique, M.Z., Farooq, M.: Sms spam detection by operating on byte-level distributions using hidden markov models (hmms). In: Virus Bulletin International Conference (2010)
The sms spam collection v1. http://www.dt.fee.unicamp.br/tiago/smsspamcollection/
Xiang, Y., Chowdhury, M., Ali, S.: Filtering mobile spam by support vector machine. In: Conference on Computer Sciences, Software Engineering, Information Technology, E-Business and Applications, pp. 1–4 (2004)
Yadav, K., Kumaraguru, P., Goyal, A., Gupta, A., Naik, V.: Smsassassin: crowdsourcing driven mobile-based system for sms spam filtering. In: Mobile Computing Systems and Applications, HotMobile, pp. 1–6 (2011)
Acknowledgments
The authors would like to thank the authors of [17] for providing the SMS dataset.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Goswami, G., Singh, R., Vatsa, M. (2016). Automated Spam Detection in Short Text Messages. In: Singh, R., Vatsa, M., Majumdar, A., Kumar, A. (eds) Machine Intelligence and Signal Processing. Advances in Intelligent Systems and Computing, vol 390. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2625-3_8
Download citation
DOI: https://doi.org/10.1007/978-81-322-2625-3_8
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2624-6
Online ISBN: 978-81-322-2625-3
eBook Packages: EngineeringEngineering (R0)