Semantics-aware obfuscation scheme prediction for binary

Author:

Zhao Yujie,Tang Zhanyong,Ye Guixin,Peng Dongxu,Fang Dingyi,Chen Xiaojiang,Wang Zheng

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference65 articles.

1. Drebin: Effective and explainable detection of android malware in your pocket.;Arp,2014

2. Predicting the resilience of obfuscated code against symbolic execution attacks via machine learning;Banescu,2017

3. Characterizing the Strength of Software Obfuscation Against Automated Attacks;Banescu,2017

4. On the (im) possibility of obfuscating programs;Barak,2001

5. Modelling out-of-vocabulary words for robust speech recognition;Bazzi,2002

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1. A Method to Quantitative Compare Obfuscating Ttransformations;Informatics and Automation;2024-05-28

2. Function-Level Code Obfuscation Detection Through Self-Attention-Guided Multi-Representation Fusion;International Journal of Software Engineering and Knowledge Engineering;2023-12-11

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4. Computer-Aided Reverse Engineering of Protected Software;Communications in Computer and Information Science;2023

5. Fine-Grained Obfuscation Scheme Recognition on Binary Code;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2022

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