Spam Detection Using Bidirectional Transformers and Machine Learning Classifier Algorithms | Journal of Computational and Cognitive Engineering

<!DOCTYPE html> <html lang="en" xml:lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Spam Detection Using Bidirectional Transformers and Machine Learning Classifier Algorithms | Journal of Computational and Cognitive Engineering </title> <link rel="icon" href="https://ojs.bonviewpress.com/public/journals/4/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.4.0.7"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Journal of Computational and Cognitive Engineering"/> <meta name="citation_journal_abbrev" content="JCCE"/> <meta name="citation_issn" content="2810-9503"/> <meta name="citation_author" content="Yanhui Guo"/> <meta name="citation_author_institution" content="Department of Computer Science, University of Illinois Springfield, USA"/> <meta name="citation_author" content="Zelal Mustafaoglu"/> <meta name="citation_author_institution" content="Department of Computer Science, University of Illinois Springfield, USA"/> <meta name="citation_author" content="Deepika Koundal"/> <meta name="citation_author_institution" content="Department of Systemics, University of Petroleum and Energy Studies, India"/> <meta name="citation_title" content="Spam Detection Using Bidirectional Transformers and Machine Learning Classifier Algorithms"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2023"/> <meta name="citation_volume" content="2"/> <meta name="citation_issue" content="1"/> <meta name="citation_firstpage" content="5"/> <meta name="citation_lastpage" content="9"/> <meta name="citation_doi" content="10.47852/bonviewJCCE2202192"/> <meta name="citation_abstract_html_url" content="https://ojs.bonviewpress.com/index.php/JCCE/article/view/192"/> <meta name="citation_abstract" xml:lang="en" content="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. 聽 Received: 16 March 2022 | Revised: 21 April 2022 | Accepted: 22 April 2022 聽 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."/> <meta name="citation_keywords" xml:lang="en" content="spam detection"/> <meta name="citation_keywords" xml:lang="en" content="transfer learning"/> <meta name="citation_keywords" xml:lang="en" content=" transformer"/> <meta name="citation_keywords" xml:lang="en" content="BERT"/> <meta name="citation_keywords" xml:lang="en" content="classifier"/> <meta name="citation_keywords" xml:lang="en" content="machine learning"/> <meta name="citation_pdf_url" content="https://ojs.bonviewpress.com/index.php/JCCE/article/download/192/131"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Yanhui Guo"/> <meta name="DC.Creator.PersonalName" content="Zelal Mustafaoglu"/> <meta name="DC.Creator.PersonalName" content="Deepika Koundal"/> <meta name="DC.Date.created" scheme="ISO8601" content="2022-04-24"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2022-03-16"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2023-02-22"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-01-10"/> <meta name="DC.Description" xml:lang="en" content="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. 聽 Received: 16 March 2022 | Revised: 21 April 2022 | Accepted: 22 April 2022 聽 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."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="192"/> <meta name="DC.Identifier.pageNumber" content="5-9"/> <meta name="DC.Identifier.DOI" content="10.47852/bonviewJCCE2202192"/> <meta name="DC.Identifier.URI" content="https://ojs.bonviewpress.com/index.php/JCCE/article/view/192"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2022 Authors"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/4.0/"/> <meta name="DC.Source" content="Journal of Computational and Cognitive Engineering"/> <meta name="DC.Source.ISSN" content="2810-9503"/> <meta name="DC.Source.Issue" content="1"/> <meta name="DC.Source.Volume" content="2"/> <meta name="DC.Source.URI" content="https://ojs.bonviewpress.com/index.php/JCCE"/> <meta name="DC.Subject" xml:lang="en" content="spam detection"/> <meta 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class="cmp_breadcrumbs" role="navigation" aria-label="You are here:"> <ol> <li> <a href="https://ojs.bonviewpress.com/index.php/JCCE/index"> Home </a> <span class="separator">/</span> </li> <li> <a href="https://ojs.bonviewpress.com/index.php/JCCE/issue/archive"> Archives </a> <span class="separator">/</span> </li> <li> <a href="https://ojs.bonviewpress.com/index.php/JCCE/issue/view/35"> Vol. 2 No. 1 (2023) </a> <span class="separator">/</span> </li> <li class="current" aria-current="page"> <span aria-current="page"> Research Articles </span> </li> </ol> </nav> <article class="obj_article_details"> <h1 class="page_title"> Spam Detection Using Bidirectional Transformers and Machine Learning Classifier Algorithms </h1> <div class="row"> <div class="main_entry"> <section class="item authors"> <h2 class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> Yanhui Guo </span> <span class="affiliation"> Department of Computer Science, University of Illinois Springfield, USA </span> </li> <li> <span class="name"> Zelal Mustafaoglu </span> <span class="affiliation"> Department of Computer Science, University of Illinois Springfield, USA </span> </li> <li> <span class="name"> Deepika Koundal </span> <span class="affiliation"> Department of Systemics, University of Petroleum and Energy Studies, India </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.47852/bonviewJCCE2202192"> https://doi.org/10.47852/bonviewJCCE2202192 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> spam detection, transfer learning, transformer, BERT, classifier, machine learning </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>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.</p> <p>聽</p> <p><strong>Received</strong>: 16 March 2022 | <strong>Revised</strong>: 21 April 2022 | <strong>Accepted</strong>: 22 April 2022</p> <p>聽</p> <p><strong>Conflicts of Interest</strong></p> <p>Yanhui Guo is an editorial board member for <em>Journal of Computational and Cognitive Engineering</em>, 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.</p> </section> <br /><div class="separator"></div><div class="item abstract" id="trendmd-suggestions"></div><script defer src='//js.trendmd.com/trendmd.min.js' data-trendmdconfig='{"website_id":"89267", "element":"#trendmd-suggestions"}'></script><div class="item downloads_chart"> <h3 class="label"> Metrics </h3> <div id="paperbuzz"><div id="loading">Metrics Loading ...</div></div> <script type="text/javascript"> window.onload = function () { var options = { paperbuzzStatsJson: JSON.parse('{\"altmetrics_sources\":[{\"events\":null,\"events_count\":2574,\"events_count_by_day\":[{\"count\":\"2\",\"date\":\"2022-04-24\"},{\"count\":\"3\",\"date\":\"2022-04-25\"},{\"count\":\"1\",\"date\":\"2022-04-26\"},{\"count\":\"6\",\"date\":\"2022-04-30\"},{\"count\":\"1\",\"date\":\"2022-05-02\"},{\"count\":\"1\",\"date\":\"2022-05-03\"},{\"count\":\"1\",\"date\":\"2022-05-05\"},{\"count\":\"2\",\"date\":\"2022-05-06\"},{\"count\":\"1\",\"date\":\"2022-05-07\"},{\"count\":\"1\",\"date\":\"2022-05-08\"},{\"count\":\"1\",\"date\":\"2022-05-09\"},{\"count\":\"2\",\"date\":\"2022-05-11\"},{\"count\":\"2\",\"date\":\"2022-05-12\"},{\"count\":\"4\",\"date\":\"2022-05-13\"},{\"count\":\"1\",\"date\":\"2022-05-14\"},{\"count\":\"1\",\"date\":\"2022-05-15\"},{\"count\":\"1\",\"date\":\"2022-05-16\"},{\"count\":\"2\",\"date\":\"2022-05-17\"},{\"count\":\"3\",\"date\":\"2022-05-18\"},{\"count\":\"1\",\"date\":\"2022-05-20\"},{\"count\":\"2\",\"date\":\"2022-05-24\"}],\"events_count_by_month\":[{\"count\":\"12\",\"date\":\"2022-04\"},{\"count\":\"40\",\"date\":\"2022-05\"},{\"count\":\"22\",\"date\":\"2022-06\"},{\"count\":\"47\",\"date\":\"2022-07\"},{\"count\":\"18\",\"date\":\"2022-08\"},{\"count\":\"34\",\"date\":\"2022-09\"},{\"count\":\"10\",\"date\":\"2022-10\"},{\"count\":\"28\",\"date\":\"2022-11\"},{\"count\":\"25\",\"date\":\"2022-12\"},{\"count\":\"36\",\"date\":\"2023-01\"},{\"count\":\"74\",\"date\":\"2023-02\"},{\"count\":\"115\",\"date\":\"2023-03\"},{\"count\":\"139\",\"date\":\"2023-04\"},{\"count\":\"113\",\"date\":\"2023-05\"},{\"count\":\"83\",\"date\":\"2023-06\"},{\"count\":\"80\",\"date\":\"2023-07\"},{\"count\":\"92\",\"date\":\"2023-08\"},{\"count\":\"119\",\"date\":\"2023-09\"},{\"count\":\"112\",\"date\":\"2023-10\"},{\"count\":\"89\",\"date\":\"2023-11\"},{\"count\":\"77\",\"date\":\"2023-12\"},{\"count\":\"58\",\"date\":\"2024-01\"},{\"count\":\"44\",\"date\":\"2024-02\"},{\"count\":\"109\",\"date\":\"2024-03\"},{\"count\":\"156\",\"date\":\"2024-04\"},{\"count\":\"114\",\"date\":\"2024-05\"},{\"count\":\"97\",\"date\":\"2024-06\"},{\"count\":\"84\",\"date\":\"2024-07\"},{\"count\":\"106\",\"date\":\"2024-08\"},{\"count\":\"83\",\"date\":\"2024-09\"},{\"count\":\"90\",\"date\":\"2024-10\"},{\"count\":\"83\",\"date\":\"2024-11\"},{\"count\":\"70\",\"date\":\"2024-12\"},{\"count\":\"92\",\"date\":\"2025-01\"},{\"count\":\"23\",\"date\":\"2025-02\"}],\"events_count_by_year\":[{\"count\":\"236\",\"date\":\"2022\"},{\"count\":\"1129\",\"date\":\"2023\"},{\"count\":\"1094\",\"date\":\"2024\"},{\"count\":\"115\",\"date\":\"2025\"}],\"source\":{\"display_name\":\"File downloads\"},\"source_id\":\"fileDownloads\"},{\"events\":[{\"author\":null,\"occurred_at\":\"2024-03-20T17:10:40.000Z\",\"url\":\"https:\\/\\/doi.org\\/10.2298\\/tsci231116001g\"}],\"events_count\":1,\"events_count_by_day\":[{\"count\":1,\"date\":\"2024-03-20\"}],\"events_count_by_month\":[{\"count\":1,\"date\":\"2024-03\"}],\"events_count_by_year\":[{\"count\":1,\"date\":\"2024\"}],\"first_event_date\":\"2024-03-20\",\"source\":{\"display_name\":\"Crossref\",\"icon_url\":\"https:\\/\\/api.paperbuzz.org\\/static\\/img\\/favicons\\/crossref.ico\",\"id\":\"crossref\"},\"source_id\":\"crossref\"}],\"crossref_event_data_url\":\"https:\\/\\/api.eventdata.crossref.org\\/v1\\/events?rows=1000&filter=from-collected-date:1990-01-01,until-collected-date:2099-01-01,obj-id:10.47852\\/bonviewjcce2202192\",\"doi\":\"10.47852\\/bonviewjcce2202192\",\"metadata\":{\"DOI\":\"10.47852\\/bonviewjcce2202192\",\"ISSN\":[\"2810-9503\"],\"URL\":\"http:\\/\\/dx.doi.org\\/10.47852\\/bonviewjcce2202192\",\"abstract\":\"<jats:p>Spam email has accounted for a high percentage of email traffic and has created problems worldwide. 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Spam Detection Using Bidirectional Transformers and Machine Learning Classifier Algorithms | Journal of Computational and Cognitive Engineering