[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119469-en":3,"doc-seo-119469-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119469,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","An Efficient and Robust Framework for IoT Security using Machine Learning Techniques","Spotting and estimating malicious node(s) in sensor-based networks remains an open challenge. The presented study focuses on identifying malicious nodes in IoT networks using machine learning models. The SensorNetGuard dataset was used to train and test Decision Tree, Support Vector Machines, K-Nearest Neighbor, and Random Forest. The Random Forest model achieved top performance with accuracy, recall, ROC AUC, precision, F1-score of 99.99% and Cohen’s Kappa of 0.99, supporting real-time IoT security; the dataset is planned for public release on IEEE DataPort and Kaggle.","[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia Computer Science 258 (2025) 118–124  \nInternational Conference on Machine Learning and Data Engineering An Efficient and Robust Framework for IoT Security using Machine  \nLearning Techniques  \nVivek Kumar Pandeya , Shiv Prakasha,∗, Sudhanshu Kumar Jhaa , Tiansheng Yangb ,  \nRajkumar Singh Rathorec  \na University of Allahabad, Department of Electronics and Communication, Prayagraj, India b University of South Wales, Faculty of Business and Creative Industries, Pontypridd, United Kingdom  \nc Cardiff Metropoliton University, Cardiff School of Technologies, Cardiff, United Kingdom  \nAbstract  \nSpotting and approximation of malicious node(s) in sensor based network is an open challenge. The proposed research work presented here primarily focuses on identification and estimation of malicious nodes within IoT networks following a machine learning-based models. The SensorNetGuard dataset was employed for the development and testing of the machine learning models such as Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) etc. The presented model here has been developed and evaluated using Python libraries like Scikit-learn, Seaborn, Matplotlib, and Pandas. In this work, Random Forest model has been emerged as a most effective model in detecting malicious nodes and shows an accuracy, recall, ROC AUC, precision, and F1-score of 99.99% and Cohen’s Kappa of 0.99 . This depicts the capability of machine learning performance toward real-time IoT security. The SensorNetGuard dataset will be publicly available on platforms like IEEE DataPort and Kaggle to enable further research.  \n© 2025 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the International Conference on Machine Learning and Data Engineering  \nKeywords: WSN, Malicious Node, Intrusion Detection System, SensorNetGuard, Decision Tree  \n1. Introduction  \nIoT, driven by three dimensions—scale, scope, and intelligence—has rapidly increased the number of connected devices [1] . With this surge, questions of security, privacy, and policy frameworks arise to meet the challenges posed in a number of industries that illustrate threats [2] . In the last five years, there has been a tremendous increase in cyber attacks on IoT networks, which has necessitated the development of new security tools. However, due to its constraints alone, many traditional security methods are unsuitable for IoT [3] . Recognizing malicious nodes as quickly as possible in IoT networks is essential. If left unnoticed, malicious nodes cause enormous destruction in network per-  \n∗ Corresponding author. Tel.: +919958292072; fax: +91-05315252542 E-mail address: [shivprakash@allduniv.ac.in](shivprakash@allduniv.ac.in)  \n1877-0509 © 2025 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the International Conference on Machine Learning and Data Engineering  \n10.1016/j.procs.2025.04.205  \nVivek Kumar Pandey et al. / Procedia Computer Science 258 (2025) 118–124 119  \nformance. Various solutions have been offered for the detection and isolation of such nodes. However, the deployment security in IoT environments becomes very intricate due to nodes being remotely located and resource-constrained [4] .  \nSupervised ML classification algorithms predict the labels given specific inputs. Binary classification has many applications in tasks that demand a choice between two mutually exclusive outcomes and finds a major application a","cbCaibfKRWQQQVdI","https://ap.wps.com/l/cbCaibfKRWQQQVdI","pdf",576597,1,7,"English","en",105,"# Introduction\n## Inspiration and Impact\n## Innovation\n# Literature Review\n## Intrusion recognition in IoT using ML/DL\n# Methodology\n## Dataset and ML techniques\n## Proposed model flow\n# Experimental setup and results\n## Discussion on obtained results\n# Conclusion and future research directions","[{\"question\":\"What problem does the framework address in IoT networks?\",\"answer\":\"It targets the identification and estimation of malicious node(s) in sensor-based IoT networks, where missed threats can severely degrade network performance.\"},{\"question\":\"Which dataset and models are used in the research?\",\"answer\":\"The SensorNetGuard dataset is used, and multiple supervised machine learning models are evaluated, including Decision Tree, SVM, KNN, and Random Forest.\"},{\"question\":\"Why is Random Forest highlighted as the most effective model?\",\"answer\":\"Random Forest shows the strongest detection performance, reporting accuracy, recall, ROC AUC, precision, and F1-score of 99.99% and Cohen’s Kappa of 0.99.\"}]","An Efficient and Robust Framework for IoT Security using Machine Learning Techniques | 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problem does the framework address in IoT networks?","Question",{"text":75,"@type":76},"It targets the identification and estimation of malicious node(s) in sensor-based IoT networks, where missed threats can severely degrade network performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and models are used in the research?",{"text":80,"@type":76},"The SensorNetGuard dataset is used, and multiple supervised machine learning models are evaluated, including Decision Tree, SVM, KNN, and Random Forest.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is Random Forest highlighted as the most effective model?",{"text":84,"@type":76},"Random Forest shows the strongest detection performance, reporting accuracy, recall, ROC AUC, precision, and F1-score of 99.99% and Cohen’s Kappa of 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