Abstract
The popularity of mobile devices is increasing day by day as they provide a large variety of services by reducing the cost of services. Short Message Service (SMS) is considered one of the widely used communication service. However, this has led to an increase in mobile devices attacks like SMS Spam. In this paper, we present a novel approach that can detect and filter the spam messages using machine learning classification algorithms. We study the characteristics of spam messages in depth and then found ten features, which can efficiently filter SMS spam messages from ham messages. Our proposed approach achieved 96.5% true positive rate and 1.02% false positive rate for Random Forest classification algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Mobile Commons Blog. https://www.mobilecommons.com/blog/2016/01/how-text-messaging-will-change-for-the-better-in-2016/
SMS Blocker Award. https://play.google.com/store/apps/details?id=com.smsBlocker&hl=en
TextBlocker. https://play.google.com/store/apps/details?id=com.thesimpleandroidguy.app.messageclient&hl=en
Androidapp. https://play.google.com/store/apps/details?id=com.mrnumber.blocker&hl=en
Puniškis, D., Laurutis, R., Dirmeikis, R.: An artificial neural nets for spam e-mail recognition. Elektronika ir Elektrotechnika 69, 73–76 (2006)
Jain, A.K., Gupta, B.B.: Phishing detection: analysis of visual similarity based approaches. Secur. Commun. Netw. 2017 (2017). Article ID 5421046. doi:10.1155/2017/5421046
Gupta, B.B., Tewari, A., Jain, A.K., Agrawal, D.P.: Fighting against phishing attacks: state of the art and future challenges. Neural Comput. Appl. 1–26 (2016). doi:10.1007/s00521-016-2275-y
Jain, A.K., Gupta, B.B.: A novel approach to protect against phishing attacks at client side using auto-updated white-list. EURASIP J. Inf. Secur. 1–11 (2016). doi:10.1186/s13635-016-0034-3
Choudhary, N., Jain, A.K.: Comparative Analysis of Mobile Phishing Detection and Prevention Approaches (Accepted)
Tatango Learning Center. https://www.tatango.com/blog/top-25-sms-spam-area-codes/
Adaptive Mobile Press Releases. https://www.adaptivemobile.com/press-centre/press-releases/five-top-spam-texts-for-2012-revealed-in-adaptivemobiles-ongoing-threat-ana
Cloudmark Report. https://www.tatango.com/blog/sms-spammers-exploit-twilio-send-385000-spam-text-messages/
Action Fraud News. http://www.actionfraud.police.uk/news/latest-scams-to-watch-out-for-apr16
ACMA Cybersecurity Blog. http://www.acma.gov.au/theACMA/engage-blogs/engage-blogs/Cybersecurity/Banks-targetted-by-SMS-phishing-scam
El-Alfy, E.S.M., AlHasan, A.A.: Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm. Future Gen. Comput. Syst. 64, 98–107 (2016). doi:10.1016/j.future.2016.02.018
Jialin, M., Zhang, Y., Liu, J., Yu, K., Wang, X.: Intelligent SMS spam filtering using topic model. In: International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 380–383. IEEE (2016). doi:10.1109/INCoS.2016.47
Chan, P.P.K., Yang, C., Yeung, D.S., Ng, W.W.Y.: Spam filtering for short messages in adversarial environment. Neurocomputing 155, 167–176 (2015). doi:10.1016/j.neucom.2014.12.034
Delany, S.J., Buckley, M., Greene, D.: SMS spam filtering: methods and data. Expert Syst. Appl. 39, 9899–9908 (2012). doi:10.1016/j.eswa.2012.02.053
Xu, Q., Xiang, E.W., Yang, Q., Du, J., Zhong, J.: SMS spam detection using non-content features. IEEE Intell. Syst. 27(6), 44–51 (2012)
Nuruzzaman, M.T., Lee, C., Abdullah, M., Choi, D.: Simple SMS spam filtering on independent mobile phone. Secur. Commun. Netw. 1209–1220 (2012). doi:10.1002/sec.577
Uysal, A.K., Gunal, S., Ergin, S., Gunal, E.S.: A novel framework for SMS spam filtering. In: International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 1–4. IEEE (2012). doi:10.1109/INISTA.2012.6246947
Yadav, K., Kumaraguru, P., Goyal, A., Gupta, A., Naik, V.: SMSAssassin: crowdsourcing driven mobile-based system for SMS spam filtering. In: 12th Workshop on Mobile Computing Systems and Applications, pp. 1–6. ACM (2011). doi:10.1145/2184489.2184491
Hidalgo, J.M.G., Bringas, G.C., Sánz, E.P., García, F.C.: Content based SMS spam filtering. In: ACM Symposium on Document Engineering, pp. 107–114. ACM (2006). doi:10.1145/1166160.1166191
SMS Spam Corpus. http://www.esp.uem.es/jmgomez/smsspamcorpus
Cormack, G.V., Hidalgo, J.M.G., Sánz, E.P.: Feature engineering for mobile (SMS) spam filtering. In: 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 871–872. ACM (2007). doi:10.1145/1277741.1277951
Cormack, G.V., Hidalgo, J.M.G., Sánz, E.P.: Spam filtering for short messages. In: 16th ACM Conference on Conference on Information and Knowledge Management, pp. 313–320. ACM (2007). doi:10.1145/1321440.1321486
Ayodele, T.O.: Types of machine learning algorithms. In: New Advances in Machine Learning. INTECH Publisher (2010)
Machine Algorithm Algorithms. http://machinelearningmastery.com/naive-bayes-for-machine-learning
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Choudhary, N., Jain, A.K. (2017). Towards Filtering of SMS Spam Messages Using Machine Learning Based Technique. In: Singh, D., Raman, B., Luhach, A., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2017. Communications in Computer and Information Science, vol 712. Springer, Singapore. https://doi.org/10.1007/978-981-10-5780-9_2
Download citation
DOI: https://doi.org/10.1007/978-981-10-5780-9_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5779-3
Online ISBN: 978-981-10-5780-9
eBook Packages: Computer ScienceComputer Science (R0)