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 Dominic Andrew, P.; Jose, Anna Mariya; Jayapandian, N.; Angel, Chinnapalli Neha; Brar, Krishjeet Title A name given to the resource Advanced Malware Analysis and Detection Using Deep Neural Networks 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 Conference Proceedings - 2025 IEEE 4th International Conference on Data, Decision and Systems, ICDDS 2025;pp.7-12 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDDS67737.2025.11344689" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDDS67737.2025.11344689</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033355386?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033355386?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 Dominic Andrew P., Christ University, Department of Cse, Bangalore, India; Jose A.M., Christ University, Department of Cse, Bangalore, India; Jayapandian N., Christ University, Department of Cse, Bangalore, India; Angel C.N., Christ University, Department of Cse, Bangalore, India; Brar K., Christ University, Department of Cse, Bangalore, India Description An account of the resource Malware is malicious software that is used to cause harm to the computer systems, networks or users across several operating systems, such as Windows, macOS, iOS, Android and Linux. The identification and categorisation of malware is a difficult subject with no one-size-fits-all solution due to the constant evolution of the malware and lack of standardised detection frameworks. The use of deep learning models in cybersecurity encourages the growth of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (IML) techniques. The goal of this research is to automate the way of analysing malware using a simple framework without the need of complex software. This article focuses on the application of Neural Networks, a deep learning model in detecting and analysing the behavior of malware. The model performed better when compared to other techniques by achieving an accuracy of 99.43%, precision of 99.05% and a F1 score of 0.99, which was trained on a large dataset containing 1,38,048 samples. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Cyber-attack; Deep Learning; Machine Learning; Malware Detection; Neural Network; Random Forest Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155479-8; 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