20.500.12556/DiRROS-22510 Comprehensive benchmarking of knowledge graph embeddings methods for Android malware detection The rising popularity and open-source model of the Android operating system has made it a main target for attackers creating malware applications. With the mobile industry being an expanding device ecosystem, there is a critical need for developing effective methods to protect against mobile malware. Recognizing the latest approaches and their limitations, we have conducted a comprehensive empirical analysis on the applicability of knowledge graphs for malware detection in view of the influence of the scoring functions, the vector dimension, the stability of the obtained results, the performance of the individual classifiers, and other important time dependencies. In addition, we propose a knowledge-graph based method aimed at improving the quality of classification input data, while offering greater interfacing capabilities with external knowledge and lower computational complexity. The proposed method offers a new perspective on working with Android malware, demonstrating a unique data processing pipeline for malware sample identification and encouraging further innovation in the field. Our findings demonstrate that knowledge graph representation is not only feasible, but also provides well-performing results, remaining competitive with state-of-the-art approaches. mobile android security knowledge graphs embeddings machine learning strojno učenje mobilne naprave Android varnost true false true Elsevier Nizozemska Angleški jezik Slovenski jezik © 2025 The Authors. Neznano 2025-05-26 12:41:29 2025-05-26 12:41:30 2025-10-24 03:51:25 0000-00-00 00:00:00 2025 0 0 Nasl. z nasl. zaslona; Opis vira z dne 12. 5. 2025; 1-13 str. Vol. 288, 127888 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-11-13 2025-04-24 2025-05-21 621.395 1873-6793 10.1016/j.eswa.2025.127888 237162499 https://www.sciencedirect.com/science/article/pii/S0957417425015106?via=ihub 1 fd0b1e8b-3a1d-11f0-963a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=32535 RAZ_Kincl_Jan_2025.pdf RAZ_Kincl_Jan_2025.pdf 1 4426BA9B42AECF24DEAE0300D7AE50B2 e266b4b04bd99a4cc93aa60c42fc96fd670a791da0b4bb66f823f72243ec816f abf954fc-3a1e-11f0-963a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=32537 Institut Jožef Stefan 0 0 0