Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Anand, T.R.; Priya, Mohana T; Senthilkumar, Poorana S; Vijayalakshmi, P.S.; Thirunavukkarasu, V.; Rajesh Kannae, R. Title A name given to the resource Performance Evaluation of Machine Learning Models for Detecting Vulnerabilities in Internet of Things Network Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Drug Delivery Technology;Volume;16;Issue;2;pp.627-638 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.25258/ijddt.16.627-638" target="_blank" rel="noreferrer noopener">https://doi.org/10.25258/ijddt.16.627-638</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034584009?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034584009?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Anand T.R., Department of Computer Technology, Dr. N. G. P. Arts and Science College, Tamil Nadu, Coimbatore, India; Priya M.T., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Senthilkumar P.S., Department of Computer Applications, Dr. N. G. P. Arts and Science College, Tamil Nadu, Coimbatore, India; Vijayalakshmi P.S., Department of Computer Applications, Dr. N. G. P. Arts and Science College, Tamil Nadu, Coimbatore, India; Thirunavukkarasu V., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Rajesh Kannae R., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India Description An account of the resource Security threats and attacks are a growing concern in the field of Internet of Things (IoT) infrastructure. Internet-based automated network application models are used across various domains; commensurately, different security vulnerabilities and anomaly attacks are also increased at the same level. These attacks could cause failures in IoT infrastructure and network systems. In the modern world, Machine Learning (ML) models support various predictive analyses, providing more accurate results for future forecasting in various fields. In this article, we compare existing classical Machine Learning (ML) algorithms supported by Artificial Intelligence (AI) to evaluate and predict the performance and accuracy of different vulnerabilities in IoT infrastructure. We considered and compared the results of Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN) using publicly available datasets. Through this evaluation, we obtained an accuracy of 99.4% from DT, RF, and ANN. Additionally, RF demonstrated a highest accuracy of F1 is 0.994 and lowest STD variance is 0.014 than compared models in the selected dataset. 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved. Subject The topic of the resource Attacks and detection; Dataset; Internet of Things; Machine Learning; Vulnerabilities Publisher An entity responsible for making the resource available Dr. Yashwant Research Labs Pvt. Ltd. Relation A related resource ISSN: 9754415; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online