Spam Detection Using Bidirectional Transformers and Machine Learning Classifier Algorithms

Yanhui Guo; Zelal  Mustafaoglu; Deepika  Koundal

doi:10.47852/bonviewJCCE2202192

Authors

Yanhui Guo Department of Computer Science, University of Illinois Springfield, USA
Zelal Mustafaoglu Department of Computer Science, University of Illinois Springfield, USA
Deepika Koundal Department of Systemics, University of Petroleum and Energy Studies, India

DOI:

https://doi.org/10.47852/bonviewJCCE2202192

Keywords:

spam detection, transfer learning, transformer, BERT, classifier, machine learning

Abstract

Spam email has accounted for a high percentage of email traffic and has created problems worldwide. The deep learning transformer model is an efficient tool in natural language processing. This study proposed an efficient spam detection approach using a pretrained bidirectional encoder representation from transformer (BERT) and machine learning algorithms to classify ham or spam emails. Email texts were fed into the BERT, and features obtained from the BERT outputs were used to represent the texts. Four classifier algorithms in machine learning were employed to classify the features of the text into ham or spam categories. The proposed model was tested using two public datasets in the experiments. The results of the evaluation metrics demonstrate that the logistic regression algorithm achieved the best classification performance in both datasets. They also justified the efficient ability of the proposed model in detecting spam emails.

Received: 16 March 2022 | Revised: 21 April 2022 | Accepted: 22 April 2022

Conflicts of Interest

Yanhui Guo is an editorial board member for Journal of Computational and Cognitive Engineering, and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work.