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 Kumar, Deepanshu; Poonia, Ramesh Chandra; Shanbhog, Manjula Title A name given to the resource Stacked Ensemble Method of Multi Class Malware Detection Using PE Header and Section Attributes 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 Proceedings of International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDISS68238.2025.11320719" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDISS68238.2025.11320719</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033671810?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033671810?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 Kumar D., CHRIST University, Bengaluru, India; Poonia R.C., CHRIST University, Bengaluru, India; Shanbhog M., CHRIST University, Bengaluru, India Description An account of the resource Malware has now become sophisticated. The type of attacks has changed, too. To identify and remove them is now a great challenge. This paper presents a machine learning model for malware detection in windows. The Malware is detected based on the static collection of features, which includes the Portable Executable (PE) Header and Section data. Several classifiers were trained on a balanced dataset, including Logistic Regression, K-Nearest Neighbour, Support Vector Machine, Multi-Layer Perceptron, XGBoost, and Stacked Ensemble. The proposed stacking method utilises SVM, MLP, and XGBoost, with XGBoost serving as the meta-learner. The model delivered the best performance when compared with all the baseline models for an accuracy of 96.25% and an AUC of 0.9978. 2025 IEEE. Subject The topic of the resource classification; cybersecurity; Ensemble learning; machine learning; Malware detection; PE fields Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155641-9; 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 Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online