An Intrusion System for Internet of Things Security Breaches Using Machine Learning Techniques

<!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> An Intrusion System for Internet of Things Security Breaches Using Machine Learning Techniques | Artificial Intelligence and Applications </title> <link rel="icon" href="https://ojs.bonviewpress.com/public/journals/5/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="Artificial Intelligence and Applications"/> <meta name="citation_journal_abbrev" content="AIA"/> <meta name="citation_issn" content="2811-0854"/> <meta name="citation_author" content="Temitope Samson Adekunle"/> <meta name="citation_author_institution" content="Department of Computer Science, Colorado State University, USA"/> <meta name="citation_author" content="Oluwaseyi Omotayo Alabi"/> <meta name="citation_author_institution" content="Department of Mechanical Engineering, Lead City University, Nigeria"/> <meta name="citation_author" content="Morolake Oladayo Lawrence"/> <meta name="citation_author_institution" content="Department of Computer Science, Baze University, Nigeria"/> <meta name="citation_author" content="Toheeb A. Adeleke"/> <meta name="citation_author_institution" content="Department of Computer Engineering, Ladoke Akintola University of Technology, Nigeria"/> <meta name="citation_author" content="Olakunle Sunday Afolabi"/> <meta name="citation_author_institution" content="Department of Computer Science, University of Abuja, Nigeria"/> <meta name="citation_author" content="Godwin Nse Ebong"/> <meta name="citation_author_institution" content="Department of Data Science, University of Salford, UK"/> <meta name="citation_author" content="Gabriel Olumide Egbedokun"/> <meta name="citation_author_institution" content="Department of Computer Science, The Polytechnics Ibadan, Nigeria"/> <meta name="citation_author" content="Temitope A. Bamisaye"/> <meta name="citation_author_institution" content="Department of Computer Science, National Open University of Nigeria, Nigeria"/> <meta name="citation_title" content="An Intrusion System for Internet of Things Security Breaches Using Machine Learning Techniques"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2024/03/06"/> <meta name="citation_volume" content="2"/> <meta name="citation_issue" content="3"/> <meta name="citation_firstpage" content="188"/> <meta name="citation_lastpage" content="194"/> <meta name="citation_doi" content="10.47852/bonviewAIA42021780"/> <meta name="citation_abstract_html_url" content="https://ojs.bonviewpress.com/index.php/AIA/article/view/1780"/> <meta name="citation_abstract" xml:lang="en" content="Effective identification and categorization of network attacks are paramount for ensuring robust security. However, contemporary techniques often struggle to accurately discern and classify novel attack patterns. This research introduces an innovative framework designed to achieve reliable attack detection and classification by harnessing the synergistic capabilities of utilizing DenseNet convolutional neural networks and rap music analysis techniques. Our approach leverages feature extraction through the Attention Pyramid Network (RAPNet) framework, tailored to extract pertinent features from input data, alongside binary Pigeon optimization. Subsequently, we employ feature selection using the optimization algorithm (BPOA). Once the optimal features are identified, we employ the Densenet201 model to categorize attacks across various datasets, including Bot-IoT, CICIDS2017, and CICIDS2019, through deep learning methodologies. To address the challenge posed by imbalanced data, we introduce conditional generative adversarial networks for generating additional data samples for minority classes, thus mitigating the issue. In contrast to recent intrusion detection methods, our results showcase the model鈥檚 exceptional precision in detecting and categorizing achieving accuracy rates of 99.12%, 99.01%, and 99.18% for Bot-IoT, CICIDS2017, and CICIDS2019 datasets, respectively. Despite the potential benefits of a machine learning-based intrusion detection system (IDS) for Internet of Things (IoT) security, several limitations must be considered. These include the lack of standardized security protocols across various IoT devices and platforms, which makes it challenging to develop a uniform IDS. Furthermore, machine learning models, including those for intrusion detection, can be vulnerable to adversarial attacks that can circumvent or mislead the model鈥檚 decision-making process. Thus, the potential for sophisticated attacks on IoT systems must be considered when developing such a system. 聽 Received:聽22 September 2023| Revised: 17 January 2024聽| Accepted: 21 February 2024 聽 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. 聽 Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study."/> <meta name="citation_keywords" xml:lang="en" content="security"/> <meta name="citation_keywords" xml:lang="en" content="deep learning"/> <meta name="citation_keywords" xml:lang="en" content="dataset"/> <meta name="citation_keywords" xml:lang="en" content="framework"/> <meta name="citation_keywords" xml:lang="en" content="Bot-IoT"/> <meta name="citation_pdf_url" content="https://ojs.bonviewpress.com/index.php/AIA/article/download/1780/855"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Temitope Samson Adekunle"/> <meta name="DC.Creator.PersonalName" content="Oluwaseyi Omotayo Alabi"/> <meta name="DC.Creator.PersonalName" content="Morolake Oladayo Lawrence"/> <meta name="DC.Creator.PersonalName" content="Toheeb A. Adeleke"/> <meta name="DC.Creator.PersonalName" content="Olakunle Sunday Afolabi"/> <meta name="DC.Creator.PersonalName" content="Godwin Nse Ebong"/> <meta name="DC.Creator.PersonalName" content="Gabriel Olumide Egbedokun"/> <meta name="DC.Creator.PersonalName" content="Temitope A. Bamisaye"/> <meta name="DC.Date.created" scheme="ISO8601" content="2024-03-06"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2023-09-22"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-07-08"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-11-11"/> <meta name="DC.Description" xml:lang="en" content="Effective identification and categorization of network attacks are paramount for ensuring robust security. However, contemporary techniques often struggle to accurately discern and classify novel attack patterns. This research introduces an innovative framework designed to achieve reliable attack detection and classification by harnessing the synergistic capabilities of utilizing DenseNet convolutional neural networks and rap music analysis techniques. Our approach leverages feature extraction through the Attention Pyramid Network (RAPNet) framework, tailored to extract pertinent features from input data, alongside binary Pigeon optimization. Subsequently, we employ feature selection using the optimization algorithm (BPOA). Once the optimal features are identified, we employ the Densenet201 model to categorize attacks across various datasets, including Bot-IoT, CICIDS2017, and CICIDS2019, through deep learning methodologies. To address the challenge posed by imbalanced data, we introduce conditional generative adversarial networks for generating additional data samples for minority classes, thus mitigating the issue. In contrast to recent intrusion detection methods, our results showcase the model鈥檚 exceptional precision in detecting and categorizing achieving accuracy rates of 99.12%, 99.01%, and 99.18% for Bot-IoT, CICIDS2017, and CICIDS2019 datasets, respectively. Despite the potential benefits of a machine learning-based intrusion detection system (IDS) for Internet of Things (IoT) security, several limitations must be considered. These include the lack of standardized security protocols across various IoT devices and platforms, which makes it challenging to develop a uniform IDS. Furthermore, machine learning models, including those for intrusion detection, can be vulnerable to adversarial attacks that can circumvent or mislead the model鈥檚 decision-making process. Thus, the potential for sophisticated attacks on IoT systems must be considered when developing such a system. 聽 Received:聽22 September 2023| Revised: 17 January 2024聽| Accepted: 21 February 2024 聽 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. 聽 Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="1780"/> <meta name="DC.Identifier.pageNumber" content="188-194"/> <meta name="DC.Identifier.DOI" content="10.47852/bonviewAIA42021780"/> <meta name="DC.Identifier.URI" content="https://ojs.bonviewpress.com/index.php/AIA/article/view/1780"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2024 Authors"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/4.0"/> <meta name="DC.Source" 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class="page page_article"> <nav class="cmp_breadcrumbs" role="navigation" aria-label="You are here:"> <ol> <li> <a href="https://ojs.bonviewpress.com/index.php/AIA/index"> Home </a> <span class="separator">/</span> </li> <li> <a href="https://ojs.bonviewpress.com/index.php/AIA/issue/archive"> Archives </a> <span class="separator">/</span> </li> <li> <a href="https://ojs.bonviewpress.com/index.php/AIA/issue/view/86"> Vol. 2 No. 3 (2024) </a> <span class="separator">/</span> </li> <li class="current" aria-current="page"> <span aria-current="page"> Research Article </span> </li> </ol> </nav> <article class="obj_article_details"> <h1 class="page_title"> An Intrusion System for Internet of Things Security Breaches Using Machine Learning Techniques </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"> Temitope Samson Adekunle </span> <span class="affiliation"> Department of Computer Science, Colorado State University, USA </span> </li> <li> <span class="name"> Oluwaseyi Omotayo Alabi </span> <span class="affiliation"> Department of Mechanical Engineering, Lead City University, Nigeria </span> <span class="orcid"> <svg class="orcid_icon" viewBox="0 0 256 256" aria-hidden="true"> <style type="text/css"> .st0{fill:#A6CE39;} .st1{fill:#FFFFFF;} </style> <path class="st0" d="M256,128c0,70.7-57.3,128-128,128C57.3,256,0,198.7,0,128C0,57.3,57.3,0,128,0C198.7,0,256,57.3,256,128z"/> <g> <path class="st1" d="M86.3,186.2H70.9V79.1h15.4v48.4V186.2z"/> <path class="st1" d="M108.9,79.1h41.6c39.6,0,57,28.3,57,53.6c0,27.5-21.5,53.6-56.8,53.6h-41.8V79.1z M124.3,172.4h24.5 c34.9,0,42.9-26.5,42.9-39.7c0-21.5-13.7-39.7-43.7-39.7h-23.7V172.4z"/> <path class="st1" d="M88.7,56.8c0,5.5-4.5,10.1-10.1,10.1c-5.6,0-10.1-4.6-10.1-10.1c0-5.6,4.5-10.1,10.1-10.1 C84.2,46.7,88.7,51.3,88.7,56.8z"/> </g> </svg> <a href="https://orcid.org/0009-0005-0027-5930" target="_blank"> https://orcid.org/0009-0005-0027-5930 </a> </span> </li> <li> <span class="name"> Morolake Oladayo Lawrence </span> <span class="affiliation"> Department of Computer Science, Baze University, Nigeria </span> </li> <li> <span class="name"> Toheeb A. Adeleke </span> <span class="affiliation"> Department of Computer Engineering, Ladoke Akintola University of Technology, Nigeria </span> </li> <li> <span class="name"> Olakunle Sunday Afolabi </span> <span class="affiliation"> Department of Computer Science, University of Abuja, Nigeria </span> </li> <li> <span class="name"> Godwin Nse Ebong </span> <span class="affiliation"> Department of Data Science, University of Salford, UK </span> </li> <li> <span class="name"> Gabriel Olumide Egbedokun </span> <span class="affiliation"> Department of Computer Science, The Polytechnics Ibadan, Nigeria </span> </li> <li> <span class="name"> Temitope A. Bamisaye </span> <span class="affiliation"> Department of Computer Science, National Open University of Nigeria, Nigeria </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.47852/bonviewAIA42021780"> https://doi.org/10.47852/bonviewAIA42021780 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> security, deep learning, dataset, framework, Bot-IoT </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>Effective identification and categorization of network attacks are paramount for ensuring robust security. However, contemporary techniques often struggle to accurately discern and classify novel attack patterns. This research introduces an innovative framework designed to achieve reliable attack detection and classification by harnessing the synergistic capabilities of utilizing DenseNet convolutional neural networks and rap music analysis techniques. Our approach leverages feature extraction through the Attention Pyramid Network (RAPNet) framework, tailored to extract pertinent features from input data, alongside binary Pigeon optimization. Subsequently, we employ feature selection using the optimization algorithm (BPOA). Once the optimal features are identified, we employ the Densenet201 model to categorize attacks across various datasets, including Bot-IoT, CICIDS2017, and CICIDS2019, through deep learning methodologies. To address the challenge posed by imbalanced data, we introduce conditional generative adversarial networks for generating additional data samples for minority classes, thus mitigating the issue. In contrast to recent intrusion detection methods, our results showcase the model鈥檚 exceptional precision in detecting and categorizing achieving accuracy rates of 99.12%, 99.01%, and 99.18% for Bot-IoT, CICIDS2017, and CICIDS2019 datasets, respectively. Despite the potential benefits of a machine learning-based intrusion detection system (IDS) for Internet of Things (IoT) security, several limitations must be considered. These include the lack of standardized security protocols across various IoT devices and platforms, which makes it challenging to develop a uniform IDS. Furthermore, machine learning models, including those for intrusion detection, can be vulnerable to adversarial attacks that can circumvent or mislead the model鈥檚 decision-making process. Thus, the potential for sophisticated attacks on IoT systems must be considered when developing such a system.</p> <p>聽</p> <p><strong>Received:</strong>聽22 September 2023<strong>| Revised:</strong> 17 January 2024<strong>聽| Accepted:</strong> 21 February 2024</p> <p>聽</p> <p><strong>Conflicts of Interest</strong></p> <p>The authors declare that they have no conflicts of interest to this work.</p> <p>聽</p> <p><strong>Data Availability Statement</strong></p> <p>Data sharing is not applicable to this article as no new data were created or analyzed in this study.</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":"89268", "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\":372,\"events_count_by_day\":[{\"count\":\"1\",\"date\":\"2024-03-07\"},{\"count\":\"1\",\"date\":\"2024-03-12\"},{\"count\":\"1\",\"date\":\"2024-03-13\"},{\"count\":\"1\",\"date\":\"2024-03-14\"},{\"count\":\"2\",\"date\":\"2024-03-15\"},{\"count\":\"1\",\"date\":\"2024-03-18\"},{\"count\":\"1\",\"date\":\"2024-03-19\"},{\"count\":\"1\",\"date\":\"2024-03-20\"},{\"count\":\"2\",\"date\":\"2024-03-25\"},{\"count\":\"2\",\"date\":\"2024-03-26\"},{\"count\":\"1\",\"date\":\"2024-03-28\"},{\"count\":\"1\",\"date\":\"2024-03-31\"}],\"events_count_by_month\":[{\"count\":\"15\",\"date\":\"2024-03\"},{\"count\":\"14\",\"date\":\"2024-04\"},{\"count\":\"14\",\"date\":\"2024-05\"},{\"count\":\"11\",\"date\":\"2024-06\"},{\"count\":\"73\",\"date\":\"2024-07\"},{\"count\":\"71\",\"date\":\"2024-08\"},{\"count\":\"78\",\"date\":\"2024-09\"},{\"count\":\"62\",\"date\":\"2024-10\"},{\"count\":\"34\",\"date\":\"2024-11\"}],\"events_count_by_year\":[{\"count\":\"372\",\"date\":\"2024\"}],\"source\":{\"display_name\":\"File downloads\"},\"source_id\":\"fileDownloads\"}],\"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\\/bonviewaia42021780\",\"doi\":\"10.47852\\/bonviewaia42021780\",\"metadata\":{\"DOI\":\"10.47852\\/bonviewaia42021780\",\"ISSN\":[\"2811-0854\"],\"URL\":\"http:\\/\\/dx.doi.org\\/10.47852\\/bonviewaia42021780\",\"abstract\":\"<jats:p>Effective identification and categorization of network attacks are paramount for ensuring robust security. 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The full text ingestion is still in progress and will be completed at January, 2025.</p> </div> </article> <article class="block_announcements_article"> <h3 class="block_announcements_article_headline"> <a href="https://ojs.bonviewpress.com/index.php/AIA/announcement/view/87"> AIA Published Volume 2, Issue 4 on October 28, 2024 </a> </h3> <time class="block_announcements_article_date" datetime="2024-10-28"> <strong>October 28, 2024</strong> </time> <div class="block_announcements_article_content"> <p>We are excited to announce that <em><strong>Artificial Intelligence and Applications (AIA)</strong></em> published Volume 2 Issue 4 on October 28, 2024!</p> </div> </article> <a id="show-all" href="https://ojs.bonviewpress.com/index.php/AIA/announcement">Show all announcements ...</a> </div> </div> <div class="pkp_block block_keyword_cloud"> <h2 class="title">Keywords</h2> <div class="content" id='wordcloud'></div> <script> function randomColor() { var cores = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']; return cores[Math.floor(Math.random()*cores.length)]; } document.addEventListener("DOMContentLoaded", function() { var keywords = [{"text":"text detection","size":1},{"text":"text recognition","size":1},{"text":"text spotting","size":1},{"text":"text classification","size":1},{"text":"scene text","size":1},{"text":"car number plate detection","size":1},{"text":"optical character recognition","size":1},{"text":"supervised machine learning","size":1},{"text":"distributed machine learning","size":1},{"text":"anomaly detection","size":1},{"text":"structural health monitoring","size":1},{"text":"vehicle-bridge collisions","size":1},{"text":"railway bridges","size":1},{"text":"classification model","size":1},{"text":"deep learning","size":1},{"text":"polyp detection","size":1},{"text":"cnn","size":1},{"text":"image classification","size":1},{"text":"colorectal disease","size":1},{"text":"alzheimer's disease","size":1},{"text":"intervention techniques","size":1},{"text":"conventional methods","size":1},{"text":"artificial intelligence","size":1},{"text":"cognitive stimulation","size":1},{"text":"reality orientation","size":1},{"text":"reminiscence therapy","size":1},{"text":"enemy identification","size":1},{"text":" text similarity","size":1},{"text":"sentence transformer models","size":1},{"text":"natural language processing","size":1},{"text":"machine learning","size":1},{"text":"hr demand","size":1},{"text":"business","size":1},{"text":"hr management","size":1},{"text":"m-knn algorithm","size":1},{"text":"origin tool","size":1},{"text":"soliton solutions","size":1},{"text":"modified benjamin-bona-mahony equation","size":1},{"text":"ostrovsky-benjamin-bona-mahony equation","size":1},{"text":"mikhailov-novikov-wang equation","size":1},{"text":"physics informed neural networks","size":1},{"text":"cancer prediction","size":1},{"text":"prostate cancer","size":1},{"text":"unsupervised learning","size":1},{"text":"intelligent system","size":1},{"text":"cybernetics","size":1},{"text":"decision support","size":1},{"text":"baby cry","size":1},{"text":"multiple instance learning","size":1},{"text":"audio classification","size":1}]; var totalWeight = 0; var blockWidth = 300; var blockHeight = 200; var transitionDuration = 200; var length_keywords = keywords.length; var layout = d3.layout.cloud(); layout.size([blockWidth, blockHeight]) .words(keywords) .fontSize(function(d) { return fontSize(+d.size); }) .on('end', draw); var svg = d3.select("#wordcloud").append("svg") .attr("viewBox", "0 0 " + blockWidth + " " + blockHeight) .attr("width", '100%'); function update() { var words = layout.words(); fontSize = d3.scaleLinear().range([16, 34]); if (words.length) { fontSize.domain([+words[words.length - 1].size || 1, +words[0].size]); } } keywords.forEach(function(item,index){totalWeight += item.size;}); update(); function draw(words, bounds) { var width = layout.size()[0], height = layout.size()[1]; scaling = bounds ? 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