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A Comprehensive Comparative Study of Machine Learning Classifiers for Spam Filtering

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International Conference on Cyber Security, Privacy and Networking (ICSPN 2022) (ICSPN 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 599))

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Abstract

In July 2021, the daily spam count globally reached 283 billion and constitutes 84.12% of the total email volume. The increasing surge in the spam or unsolicited emails that can hamper communication has led to an intrinsic requirement for robust and reliable antispam filters. In recent years, spam filtration and monitoring have become significant concerns for mail and other internet services. Machine learning strategies are being employed to act as safeguards against internet spam. This study provides a systematic survey of spam filtering methods using machine learning techniques. Logistic Regression, Random Forest, Naive Bayes, and Decision Tree methods used for spam filtering have been compared based on precision, recall, and accuracy on a dataset composed of Twitter tweets, Facebook posts, and YouTube comments. The preliminary discussion involves a background study of the related work on spam filtering and the research gaps in the current literature. Further, a detailed discussion on each method has been provided in this study. The results of our experiments indicate that Decision Trees provide the best accuracy at 97.02% and precision at 98.83%, and Logistic Regression has the highest recall at 99.89%.

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Author information

Authors and Affiliations

  1. Chandigarh College of Engineering and Technology, Sector 26, Chandigarh, India

    Saksham Gupta, Amit Chhabra & Satvik Agrawal

  2. Kalinga Institute of Industrial Technology, Patia, Bhubaneswar, Odisha, India

    Sunil K. Singh

Authors
  1. Saksham Gupta
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  2. Amit Chhabra
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  3. Satvik Agrawal
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  4. Sunil K. Singh
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Corresponding author

Correspondence to Saksham Gupta .

Editor information

Editors and Affiliations

  1. Electronics Engineering and Telecom, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Nadia Nedjah

  2. University of Murcia, Murcia, Spain

    Gregorio Martínez Pérez

  3. Asia University, Taichung, Taiwan

    B. B. Gupta

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Gupta, S., Chhabra, A., Agrawal, S., Singh, S.K. (2023). A Comprehensive Comparative Study of Machine Learning Classifiers for Spam Filtering. In: Nedjah, N., Martínez Pérez, G., Gupta, B.B. (eds) International Conference on Cyber Security, Privacy and Networking (ICSPN 2022). ICSPN 2021. Lecture Notes in Networks and Systems, vol 599. Springer, Cham. https://doi.org/10.1007/978-3-031-22018-0_24

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