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 Anu, P.; Saseekala, M.; Mohanapriya, D.; Thamaraikannan, N.; Ponmozhi, K. Title A name given to the resource Static Analysis and Machine Learning for Runtime Library Detection in Linux Binaries 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 Communications in Computer and Information Science;Volume;2845 CCIS;pp.393-406 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-20907-8_33" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-20907-8_33</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039075897?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039075897?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 Anu P., School of Computing, SASTRA Deemed to Be University, Thanjavur, 613402, India; Saseekala M., Faculty of Computer Applications, School of Business and Management, CHRIST University, Bangalore, India; Mohanapriya D., Department of Computer Science, PSG College of Arts and Science, Coimbatore, 641014, India; Thamaraikannan N., Department of Artificial Intelligence and Machine Learning, Kongunadu Arts and Science College, Coimbatore, India; Ponmozhi K., Department of Computer Applications, SRM Valliammai Engineering College, Kattankulathur, India Description An account of the resource The upsurge of malware targeting Internet of Things (IoT) devices demands effective approaches. This work announces a new method, stimulated by MANTILLA, which influences machine learning models. Through a prominence on architecture-independent characteristics from binary procedures, the system progresses its competence to differentiate among several libraries as well as architectures. Classification accuracy is further enhanced by employing a majority voting technique such that the output of the model is robust and reliable. Besides the machine learning-based classification, the paper incorporates a malware detection module based on signature matching. This two-pronged approach enables the system to cross-check discovered runtime libraries against a large database of pre-collected malware signatures. By marking possible security threats according to this comparison, the system greatly increases its ability to identify malicious binaries, thus offering an added layer of security for IoT devices. This unification of detection and classification mechanisms plays an important role in dealing with the changing nature of malware threats. Although encouraging results were obtained through this project, more evaluation should be done for comparison of the efficiency of KNN with other models, for example, Random Forest. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Binary code analysis; Malware; Runtime library identification; Statically linked binaries Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-303220906-1; 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