SMS spam detection for Indian messages

The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. Though in most parts of the world, mobile messaging channel is currently regarded as “clean” and trusted, on the contrast recent reports clearly indicate that the volume of mobile phone spam is dramatically increasing year by year. It is an evolving setback especially in the Middle East and Asia. SMS spam filtering is a comparatively recent errand to deal such a problem. It inherits many concerns and quick fixes from Email spam filtering. However it fronts its own certain issues and problems. This paper inspires to work on the task of filtering mobile messages as Ham or Spam for the Indian Users by adding Indian messages to the worldwide available SMS dataset. The paper analyses different machine learning classifiers on large corpus of SMS messages for Indian people.

[1]  Akebo Yamakami,et al.  On the Validity of a New SMS Spam Collection , 2012, 2012 11th International Conference on Machine Learning and Applications.

[2]  Harry Zhang,et al.  The Optimality of Naive Bayes , 2004, FLAIRS.

[3]  Gunnar Rätsch,et al.  Soft Margins for AdaBoost , 2001, Machine Learning.

[4]  Deokjai Choi,et al.  Simple SMS spam filtering on independent mobile phone , 2012, Secur. Commun. Networks.

[5]  Gordon V. Cormack,et al.  Spam filtering for short messages , 2007, CIKM '07.

[6]  Baris Coskun,et al.  Mitigating SMS spam by online detection of repetitive near-duplicate messages , 2012, 2012 IEEE International Conference on Communications (ICC).

[7]  Akebo Yamakami,et al.  Contributions to the study of SMS spam filtering: new collection and results , 2011, DocEng '11.

[8]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[9]  Houshmand Shirani-mehr,et al.  SMS Spam Detection Using Machine Learning Approach , 2024, INTERNATIONAL JOURNAL OF RESEARCH IN SCIENCE AND TECHNOLOGY.

[10]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[11]  Sarah Jane Delany,et al.  SMS spam filtering: Methods and data , 2012, Expert Syst. Appl..

[12]  Tiago A. Almeida,et al.  Towards SMS Spam Filtering: Results under a New Dataset , 2013 .

[13]  José María Gómez Hidalgo,et al.  Content based SMS spam filtering , 2006, DocEng '06.