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 Conference Paper Faculty Publications- Conference Papers 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 Josephine, Helen V.L.; Rajagopal, Manikandan; Gayatri, S.; Lakshmi, K. Title A name given to the resource Enhancing Mobile Application Security Through Android Threat Classification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Procedia Computer Science;Volume;252;pp.154-164 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.procs.2024.12.017" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2024.12.017</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85219166914?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85219166914?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 Josephine H.J.V.L., Business Analytics, School of Business and Management, Chrisut University, Bangalore, 560074, India; Rajagopal M., School of Business and Management, Chrisut University, Bangalore, 560029, India; Gayatri S., School of Computer Science and Application, Reva University, Bangalore, India; Lakshmi K., School of Business and Management, Christ Univeristy, Bangalore, 560074, India Description An account of the resource The Android application market has grown significantly, offering customers an ever-growing range of features to suit a variety of purposes. Users are exchanging more and more sensitive data thanks to the widespread usage of mobile applications, therefore safeguarding personal information is crucial. But this boom has also opened the door for a corresponding rise in cybersecurity risks, especially for malware and adware that target mobile devices. It is imperative to categorise mobile applications into distinct groups such as malware, adware, and benign in order to fortify the mobile ecosystem. This project's primary objective is to create and apply cutting-edge machine learning algorithms that can precisely categorise mobile apps into groups including adware, malware, and benign apps. This will necessitate investigating various machine learning strategies and ensemble methods to improve classification accuracy and robustness. Multiple machine learning models were developed based on feature importance, utilizing various machine learning techniques. The evaluation metrics showcase the effectiveness of the final model, especially the Tuned XGBoost model. While achieving a high overall accuracy of 92.51%, the findings highlight the importance of considering diverse features beyond traditional flow-based ones, providing a more robust and complete perspective on mobile network security. 2025 The Authors. Published by Elsevier B.V. Subject The topic of the resource Classification; Cyber Security; Random Forest; SHAP; SMOTE; Supervised Mahcine learning; XG-Boost Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 18770509; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online