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Detecting ham and spam emails using feature union and supervised machine learning models

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Abstract

Spam emails are cyber nuisances that cause serious security threats including personal and financial information. Although several spam detection approaches exist, detecting new strains of spam messages is challenging that requires a reliable and efficient intelligent spam email detection approach. This study utilizes features from the text of emails to determine whether it is spam or normal. Multiple features are combined to obtain a higher accuracy for spam email detection. Experiments involve machine learning and deep learning models and the influence of data resampling is also investigated. Performance analysis is done using F1 score, recall, precision, and accuracy, as well as comparison with state-of-the-art approaches. Random forest and logistic regression achieve the highest accuracy scores 0.991 and 0.990, respectively which is much better than existing models.

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Data Availability

The datasets used in this study are publicly available at the following links https://www.kaggle.com/datasets/karthickveerakumar/spam-filterhttps://www.kaggle.com/washingtongold/spam-or-ham-emp-week-2-ml-hw-dataset

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Funding

“This research was supported by the Florida Center for Advanced Analytics and Data Science funded by Ernesto.Net (under the Algorithms for Good Grant).”

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Authors and Affiliations

  1. School of Computer Science, University College Dublin, D04 V1W8, Dublin, Ireland

    Furqan Rustam

  2. Department of CS and IT, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan

    Najia Saher & Arif Mehmood

  3. College of Engineering and Technology, Miami Dade College, Miami, FL, 33132, USA

    Ernesto Lee

  4. Department of Computer and Information Sciences, Spelman College, Atlanta, GA, USA

    Sandrilla Washington

  5. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38544, South Korea

    Imran Ashraf

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  1. Furqan Rustam
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  2. Najia Saher
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  4. Ernesto Lee
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  5. Sandrilla Washington
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Correspondence to Imran Ashraf.

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Rustam, F., Saher, N., Mehmood, A. et al. Detecting ham and spam emails using feature union and supervised machine learning models. Multimed Tools Appl 82, 26545–26561 (2023). https://doi.org/10.1007/s11042-023-14814-2

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  • DOI: https://doi.org/10.1007/s11042-023-14814-2

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