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Titlebook: Automatic Malware Analysis; An Emulator Based Ap Heng Yin,Dawn Song Book 2013 The Author(s) 2013 Binary Analysis.Dynamic Binary Instrumenta

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21#
發(fā)表于 2025-3-25 04:33:38 | 只看該作者
22#
發(fā)表于 2025-3-25 07:53:44 | 只看該作者
Hidden Code Extraction,, and then extracts the hidden code of the executable. To demonstrate its effectiveness, we implement a system, Renovo, and evaluate it with a large number of real-world malware samples. The experiments show that Renovo is accurate compared to previous work, yet practical in terms of performance.
23#
發(fā)表于 2025-3-25 15:10:15 | 只看該作者
24#
發(fā)表于 2025-3-25 17:16:22 | 只看該作者
Dynamic Binary Analysis Platform,nique, which incorporates shadow flag analysis, taint analysis, and symbolic execution. We have implemented this new architecture and the core technique in an analysis platform called .. Because of its extensibility and versatility, TEMU serves as the foundation for numerous malware analysis techniques.
25#
發(fā)表于 2025-3-25 21:27:28 | 只看該作者
26#
發(fā)表于 2025-3-26 01:38:12 | 只看該作者
27#
發(fā)表于 2025-3-26 05:31:36 | 只看該作者
28#
發(fā)表于 2025-3-26 09:39:44 | 只看該作者
Adrian-Mario Gellel,Michael T. Buchanan year. A large volume of new malware samples are discovered daily. Even worse, malware is rapidly evolving to be more sophisticated and evasive to strike against current malware analysis and defense systems. The work described in this book takes a root-cause oriented approach to the problem of autom
29#
發(fā)表于 2025-3-26 13:23:46 | 只看該作者
https://doi.org/10.1007/978-3-319-20925-8ible platform for dynamic binary analysis provides a foundation for solving these problems. To enable a variety of applications, we explore a unique design space. We aim to provide a whole-system view, take an external approach, facilitate fine-grained instrumentation, and have sufficient efficiency
30#
發(fā)表于 2025-3-26 18:21:38 | 只看該作者
https://doi.org/10.1007/978-3-319-20925-8their code. One technique commonly used is code packing as packed executables hinder code analysis. While this problem has been previously researched, the existing solutions are either unable to handle novel samples, or vulnerable to various evasion techniques. In this chapter, we propose a fully dy
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