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Titlebook: Malware Analysis Using Artificial Intelligence and Deep Learning; Mark Stamp,Mamoun Alazab,Andrii Shalaginov Book 2021 The Editor(s) (if a

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發(fā)表于 2025-3-27 00:19:03 | 只看該作者
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發(fā)表于 2025-3-27 03:35:28 | 只看該作者
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發(fā)表于 2025-3-27 05:28:52 | 只看該作者
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發(fā)表于 2025-3-27 09:43:00 | 只看該作者
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發(fā)表于 2025-3-27 15:30:02 | 只看該作者
A Survey of Intelligent Techniques for Android Malware Detectionsses the basic methodology and frameworks which classify, cluster, or extract Android malware features. (3) Exploring the dataset, harmful features, and classification results. (4) Discussing the current challenges and issues. Moreover, it discusses the most important factors, data-mining algorithms
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發(fā)表于 2025-3-27 18:20:58 | 只看該作者
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發(fā)表于 2025-3-28 01:15:37 | 只看該作者
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發(fā)表于 2025-3-28 05:35:48 | 只看該作者
On Ensemble Learningframework and empirical results are an effort to bring some sense of order to the chaos that is evident in the evolving field of ensemble learning—both within the narrow confines of the malware analysis problem, and in the larger realm of machine learning in general.
39#
發(fā)表于 2025-3-28 06:19:26 | 只看該作者
Optimizing Multi-class Classification of Binaries Based on Static Featurespiler optimized code, and both ELF and PE-files and demonstrate methods for optimizing storage and computational complexity when classifying executable files. Our findings show that a higher size N-gram is only preferable for some code simplifications, and that some code simplifications can give a v
40#
發(fā)表于 2025-3-28 13:14:27 | 只看該作者
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