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A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques

Author

Listed:
  • Ankit Kumar Jain

    (National Institute of Technology, Kurukshetra, India)

  • Sumit Kumar Yadav

    (Income Tax Department, Government of India, India)

  • Neelam Choudhary

    (National Institute of Technology, Kurukshetra, India)

Abstract

Smishing attack is generally performed by sending a fake short message service (SMS) that contains a link of the malicious webpage or application. Smishing messages are the subclass of spam SMS and these are more harmful compared to spam messages. There are various solutions available to detect the spam messages. However, no existing solution, filters the smishing message from the spam message. Therefore, this article presents a novel method to filter smishing message from spam message. The proposed approach is divided into two phases. The first phase filters the spam messages and ham messages. The second phase filters smishing messages from spam messages. The performance of the proposed method is evaluated on various machine learning classifiers using the dataset of ham and spam messages. The simulation results indicate that the proposed approach can detect spam messages with the accuracy of 94.9% and it can filter smishing messages with the accuracy of 96% on neural network classifier.

Suggested Citation

  • Ankit Kumar Jain & Sumit Kumar Yadav & Neelam Choudhary, 2020. "A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques," International Journal of E-Services and Mobile Applications (IJESMA), IGI Global, vol. 12(1), pages 21-38, January.
    • Handle: RePEc:igg:jesma0:v:12:y:2020:i:1:p:21-38
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    Cited by:

    1. Abdallah Ghourabi & Mahmood A. Mahmood & Qusay M. Alzubi, 2020. "A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages," Future Internet, MDPI, vol. 12(9), pages 1-16, September.

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