Abstract
In our civilizations, SMS continues to be an arguable communication tool despite the rattling development of protocol–predicated messaging approaches. Some businesses consider SMS as superior to e-mails for its representation. Spammers have been drawn to the pertinence of SMS for mobile phone freaks. The attacker can steal secret information by sending SMS primers to the link or directly contacting the victim. Therefore, SMS spam has generally grown in the last numberless spells with new security pitfalls parallel to SMiShing. We propose a deeper model for detecting English SMS spam communications amalgamating Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM). We also appraised other machine learning algorithms for the evaluation. The experimental findings presented in this study demonstrate that our model of CNN–LSTM exceeds other approaches with 98.40% accurateness and 98% F1-score.
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
Sarker, I.H.: Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci. 2(6), 1–20 (2021)
Sarker, I.H.: CyberLearning: effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks. Internet Things 14, 100393 (2021)
Morreale, M.: Daily SMS Mobile Usage Statistics (2017). https://www.smseagle.eu/2017/03/06/daily-sms-mobile-statistics/, Accessed 15 June 2020
Roy, P.K., Singh, J.P., Banerjee, S.: Deep learning to filter SMS Spam. Future Gener. Comput. Syst. 102, 524–533 (2020)
Tatango. Text Message Spam Infographic (2011). https://www.tatango.com/blog/textmessage-spam-infographic/, Accessed 15 June 2020
Goel, D., Jain, A.K.: Mobile phishing attacks and defence mechanisms: state of art and open research challenges. Comput. Secur. 73, 519–544 (2018)
Jain, A.K., Yadav, S.K., Choudhary, N.: A novel approach to detect spam and smishing SMS using machine learning techniques. IJESMA 12, 21–38 (2020)
Mishra, S., Soni, D.: Smishing detector: a security model to detect smishing through SMS content analysis and URL behavior analysis. Future Gener. Comput. Syst. 108, 803–815 (2020)
Zhou, C., Sun, C., Liu, Z., Lau, F.C.M.: A C-LSTM neural network for text classification. arXiv arXiv:cs.CL/1511.08630 (2015)
Joo, J.W., Moon, S.Y., Singh, S., Park, J.H.: S-Detector: an enhanced security model for detecting Smishing attack for mobile computing. Telecommun. Syst. 66, 29–38 (2017)
Arifin, D.D., Bijaksana, M.A.: Enhancing spam detection on mobile phone Short Message Service (SMS) performance using FP-growth and Naive Bayes Classifier. In: Proceedings of the 2016 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob), Bandung, Indonesia, 13–15 September 2016, pp. 80–84 (2016)
Sonowal, G., Kuppusamy, K.S.: SmiDCA: an anti-smishing model with machine learning approach. Comput. J. 61, 1143–1157 (2018)
Jain, A.K., Gupta, B.B.: Feature based approach for detection of smishing messages in the mobile environment. J. Inf. Technol. Res. 12, 17–35 (2019)
Jain, A.K., Gupta, B.: Rule-based framework for detection of smishing messages in mobile environment. Procedia Comput. Sci. 125, 617–623 (2018)
Almeida, T.A., Silva, T.P., Santos, I., Hidalgo, J.M.G.: Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filtering. Knowl. Based Syst. 108, 25–32 (2016)
SMS Spam Collection Dataset. https://www.kaggle.com/uciml/sms-spam-collection-dataset, Accessed 28 Feb 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hossain, S.M.M. et al. (2022). Spam Filtering of Mobile SMS Using CNN–LSTM Based Deep Learning Model. In: Abraham, A., et al. Hybrid Intelligent Systems. HIS 2021. Lecture Notes in Networks and Systems, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-030-96305-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-96305-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96304-0
Online ISBN: 978-3-030-96305-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)