DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware
This program is tentative and subject to change.
Recent advancements in ML and DL have significantly improved Android malware detection, yet many methodologies still rely on basic static analysis, bytecode, or function call graphs that often fail to capture complex malicious behaviors. DexBERT, a pre-trained BERT-like model tailored for Android representation learning, enriches class-level representations by analyzing Smali code extracted from APKs. However, its functionality is constrained by its inability to process multiple Smali classes simultaneously. This paper introduces DetectBERT, which integrates correlated Multiple Instance Learning (c-MIL) with DexBERT to handle the high dimensionality and variability of Android malware, enabling effective app-level detection. By treating class-level features as instances within MIL bags, DetectBERT aggregates these into a comprehensive app-level representation. Our evaluation demonstrates that DetectBERT not only surpasses existing state-of-the-art detection methods but also adapts to evolving malware threats. Moreover, the versatility of the DetectBERT framework holds promising potential for broader applications in app-level analysis and other software engineering tasks, offering new avenues for research and development.
This program is tentative and subject to change.
Thu 24 OctDisplayed time zone: Brussels, Copenhagen, Madrid, Paris change
16:00 - 17:30 | Software vulnerabilities and defectsESEM Technical Papers / ESEM Journal-First Papers / ESEM Emerging Results, Vision and Reflection Papers Track at Aula de graus (C4 Building) Chair(s): Daniela Cruzes Norwegian University of Science and Technology | ||
16:00 20mFull-paper | Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++ ESEM Technical Papers Anh Nguyen The , Triet Le The University of Adelaide, Muhammad Ali Babar School of Computer Science, The University of Adelaide DOI Pre-print | ||
16:20 20mFull-paper | Empirical Evaluation of Frequency Based Statistical Models for Estimating Killable Mutants ESEM Technical Papers Konstantin Kuznetsov Saarland University, CISPA, Alessio Gambi Austrian Institute of Technology (AIT), Saikrishna Dhiddi Passau University, Julia Hess Saarland University, Rahul Gopinath University of Sydney | ||
16:40 20mFull-paper | Reevaluating the Defect Proneness of Atoms of Confusion in Java Systems ESEM Technical Papers Guoshuai Shi University of Waterloo, Farshad Kazemi University of Waterloo, Michael W. Godfrey University of Waterloo, Canada, Shane McIntosh University of Waterloo Pre-print | ||
17:00 15mJournal Early-Feedback | Identifying concerns when specifying machine learning-enabled systems: A perspective-based approach ESEM Journal-First Papers Hugo Villamizar fortiss GmbH, Marcos Kalinowski Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Helio Côrtes Vieira Lopes PUC-Rio, Daniel Mendez Blekinge Institute of Technology and fortiss | ||
17:15 15mVision and Emerging Results | DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware ESEM Emerging Results, Vision and Reflection Papers Track Tiezhu Sun University of Luxembourg, Nadia Daoudi Luxembourg Institute of Science and Technology, Kisub Kim Singapore Management University, Singapore, Kevin Allix Independent Researcher, Tegawendé F. Bissyandé University of Luxembourg, Jacques Klein University of Luxembourg |