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Guided Retraining to Enhance the Detection of Difficult Android Malware.

, , , and . ISSTA, page 1131-1143. ACM, (2023)

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LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning., , , , , and . CoRR, (2023)A Deep Dive Inside DREBIN: An Explorative Analysis beyond Android Malware Detection Scores., , , and . ACM Trans. Priv. Secur., 25 (2): 13:1-13:28 (2022)Revisiting and Boosting State-of-the-Art ML-based Android Malware detectors.. University of Luxembourg, (2023)base-search.net (ftunivluxembourg:oai:orbilu.uni.lu:10993/54218).Guided Retraining to Enhance the Detection of Difficult Android Malware., , , and . ISSTA, page 1131-1143. ACM, (2023)A two-steps approach to improve the performance of Android malware detectors., , , and . CoRR, (2022)DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode., , , , , and . CoRR, (2021)Assessing the opportunity of combining state-of-the-art Android malware detectors., , , and . Empir. Softw. Eng., 28 (1): 22 (2023)Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection., , , and . Empir. Softw. Eng., 26 (4): 74 (2021)JuCify: A Step Towards Android Code Unification for Enhanced Static Analysis., , , , , , , , and . ICSE, page 1232-1244. ACM, (2022)JuCify: A Step Towards Android Code Unification for Enhanced Static Analysis., , , , , , , , and . CoRR, (2021)