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HEMD: a highly efficient random forest-based malware detection framework for Android.

, , , , , и . Neural Comput. Appl., 30 (11): 3353-3361 (2018)

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Tian-Hui Zhu

Hsiao-Mei Juan

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HEMD: a highly efficient random forest-based malware detection framework for Android., , , , , и . Neural Comput. Appl., 30 (11): 3353-3361 (2018)A Type-Based Blocking Technique for Efficient Entity Resolution over Large-Scale Data., , , , , , , и . J. Sensors, (2018)A multi-model ensemble learning framework for imbalanced android malware detection., , , и . Expert Syst. Appl., (декабря 2023)基于自适应惩罚函数的云工作流调度协同进化遗传算法 (Coevolutionary Genetic Algorithm of Cloud Workflow Scheduling Based on Adaptive Penalty Function)., и . 计算机科学, 45 (8): 105-112 (2018)A data cleaning method for heterogeneous attribute fusion and record linkage., , , , , и . Int. J. Comput. Sci. Eng., 19 (3): 311-324 (2019)Improved Prediction of Protein-Protein Interactions Using Descriptors Derived From PSSM via Gray Level Co-Occurrence Matrix., , , , , и . IEEE Access, (2019)DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model., , , , , и . Neurocomputing, (2018)GAResNet: A Transfer Learning based Framework for Android Malware Detection., , , и . ICKG, стр. 263-268. IEEE, (2023)In Silico Identification of Anticancer Peptides with Stacking Heterogeneous Ensemble Learning Model and Sequence Information., , , , , и . ICIC (2), том 11644 из Lecture Notes in Computer Science, стр. 313-323. Springer, (2019)