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SMS Spam Filtering Using Machine Learning Technique

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ICCCE 2020

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 698))

Abstract

Sending and receiving SMS is very ordinary thing for any individual’s daily life. But when at the moment, we receive undesirable SMS frequently that waste our time and money as well and consequently this moment gives us unpleasant feeling. If undesirable messages are to be sent to a huge volume of recipients erratically have resulted in displeasure by consumers but gives large profit to spammers. There are lots of reasons like high internet speed, very cheap smart phones and user friendly interface of mobile web and mobile applications that attracts a huge volume of mobile phone users. These are the key factors expected to shape the future of the market. This paper focuses on SMS Spam filtering techniques and compared their performance. We compared the machine learning model’s performance and finally result indicates that the Logistic Regression model performed well with accuracy reaching up to 96.59%.

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References

  1. Huang WL, Liu Y, Zhong ZQ (2009) Complex network based SMS filtering algorithm, pp. 990–996

    Google Scholar 

  2. Clerk J, Maxwell A (1892) Treatise on Electricity and Magnetism, vol 2, 3rd edn. Clarendon, Oxford, pp 68–73

    MATH  Google Scholar 

  3. Wang C, Zhang Y, Chen X, Liu Z, Shi L, Chen G, Qiu F, Ying C, Lu W (2010) A behavior-based SMS antispam system. IBM J Res Deve 54(6):3-1

    Google Scholar 

  4. Haykin SS (2009) Neural Networks and Learning Machines. Prentice Hall, Upper Saddle River

    Google Scholar 

  5. Rokach L (2010) Ensemble-based classifiers. Arti Intell Rev 33:1–39

    Article  Google Scholar 

  6. Healy M, Delany S, Zamolotskikh A (2005) An assessment of case-based reasoning for short text message classification. In: Proceedings of 16th Irish Conference on Artificial Intelligence and Cognitive Science, pp 257–266

    Google Scholar 

  7. Duan LZ, Li A, Huang LJ (2009) A new spam short message classification. In: Proceeding of the 1st International Workshop on Education Technology and Computer Science, pp 168–171

    Google Scholar 

  8. Zheng X, Liu C, Zou Y (2010) Chinese short messages service spam filtering based on logistic regression. J Heilongjiang Inst Technol 4(24):36–39

    Google Scholar 

  9. Gómez Hidalgo JM (2002) Evaluating cost-sensitive unsolicited bulk email categorization. In: Proceedings of the 17th ACM Symposium on Applied Computing, Madrid, Spain, pp 615–620

    Google Scholar 

  10. Zhang L, Zhu J, Yao T (2004) An evaluation of statistical spam filtering techniques. ACM Trans Asian Lang Inf Process 3(4):243–269

    Article  Google Scholar 

  11. Cormack G (2008) Email spam filtering: a systematic review. Found Trends Inf Retrieval 1(4):335–455

    Article  Google Scholar 

  12. Almeida TA, Yamakami A, Almeida J (2009) Evaluation of approaches for dimensionality reduction applied with naive bayes anti-spam filters. In: Proceedings of the 8th IEEE International Conference on Machine Learning and Applications, Miami, FL, USA, 2009, pp 517–522

    Google Scholar 

  13. Almeida TA, Yamakami A, Almeida J (2010) Filtering spams using the minimum description length principle. In: Proceedings of the 25th ACM Symposium on Applied Computing, Sierre, Switzerland, 2010, pp 1856–1860

    Google Scholar 

  14. Almeida TA, Yamakami A, Almeida J (2010) Probabilistic anti-spam filtering with dimensionality reduction. In: Proceedings of the 25th ACM Symposium on Applied Computing, Sierre, Switzerland, 2010, pp 1804–1808

    Google Scholar 

  15. Almeida TA, Yamakami A (2010) Content-based spam filtering. In: Proceedings of the 23rd IEEE International Joint Conference on Neural Networks, Barcelona, Spain, 2010, pp 1–7

    Google Scholar 

  16. Almeida TA, Almeida J, Yamakami A (2011) Spam filtering: how the dimensionality reduction affects the accuracy of naive bayes Classifiers. J Internet Serv Appl 1(3):183–200

    Article  Google Scholar 

  17. Almeida TA, Yamakami A (2012) Facing the spammers: a very effective approach to avoid junk e-mails. Exp Syst Appl 39:6557–6561

    Article  Google Scholar 

  18. Gómez Hidalgo JM, Cajigas Bringas G, Puertas Sanz E, Carrero García F (2006) Content based SMS spam filtering. In: Proceedings of the 2006 ACM Symposium on Document Engineering, Amsterdam, The Netherlands, 2006, pp 107–114

    Google Scholar 

  19. Cormack GV, Gómez Hidalgo JM, Puertas Sanz E (2007) Feature engineering for mobile (SMS) spam filtering. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, 2007, pp 871–872

    Google Scholar 

  20. Gómez Hidalgo JM, Cajigas Bringas G, Puertas Sanz E (2007) Spam filtering for short messages. In: Proceedings of the 16th ACM Conference on Conference on information and Knowledge Management, Lisbon, Portugal, 2007, pp 313–320

    Google Scholar 

  21. Liu W, Wang T (2010) Index-based online text Classification for SMS spam filtering. J Comput 5(6):844–851

    Article  Google Scholar 

  22. Lee J, Hsieh M (2011) An interactive mobile SMS confirmation method using secret sharing technique. Comput Secur 30(8):830–839

    Article  Google Scholar 

  23. Vallés E, Rosso P (2011) Detection of near-duplicate user generated contents: the sms spam collection. In: Proceedings of the 3rd International CIKM Workshop on Search and Mining User-Generated Contents, 2011, pp 27–33

    Google Scholar 

  24. Almeida T, Gómez Hidalgo J, Yamakami A (2011) Contributions to the study of SMS spam filtering: new collection and results. In: Proceedings of the 2011 ACM Symposium on Document Engineering, Mountain View, CA, USA, 2011, pp 259–262

    Google Scholar 

  25. Rashid E, Ansari MD, Gunjan VK, Khan M (2020) Enhancement in teaching quality methodology by predicting attendance using machine learning technique. In: Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough, pp. 227–235. Springer, Cham

    Google Scholar 

  26. Goel AK, Chakraborty R, Agarwal M, Ansari MD, Gupta SK, Garg D (2019) Profit or Loss: a long short term memory based model for the prediction of share price of DLF group in India. In: 2019 IEEE 9th International Conference on Advanced Computing (IACC), pp. 120–124. IEEE

    Google Scholar 

  27. Agarwal M, Bohat VK, Ansari MD, Sinha A, Gupta SK, Garg D (2019) A convolution neural network based approach to detect the disease in corn crop. In: 2019 IEEE 9th International Conference on Advanced Computing (IACC), pp. 176–181. IEEE

    Google Scholar 

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Author information

Authors and Affiliations

  1. ZEBEC Software Technologies Private Limited, Dehradun, Uttrakhand, India

    Arvind Kumar Vishwakarma

  2. Department of Computer Science & Engineering, CMR College of Engineering & Technology, Hyderabad, India

    Mohd Dilshad Ansari

  3. Gurukula Kangri Vishwavidyalaya, Haridwar, India

    Gaurav Rai

Authors
  1. Arvind Kumar Vishwakarma
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  2. Mohd Dilshad Ansari
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  3. Gaurav Rai
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Corresponding author

Correspondence to Arvind Kumar Vishwakarma .

Editor information

Editors and Affiliations

  1. BioAxis DNA Research Centre (P) Ltd., Hyderabad, India

    Amit Kumar

  2. Dynexsys, Sydney, NSW, Australia

    Stefan Mozar

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Vishwakarma, A.K., Ansari, M.D., Rai, G. (2021). SMS Spam Filtering Using Machine Learning Technique. In: Kumar, A., Mozar, S. (eds) ICCCE 2020. Lecture Notes in Electrical Engineering, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-15-7961-5_66

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  • DOI: https://doi.org/10.1007/978-981-15-7961-5_66

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7960-8

  • Online ISBN: 978-981-15-7961-5

  • eBook Packages: EngineeringEngineering (R0)

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