DeepVMUnProtect: Neural Network-Based Recovery of VM-Protected Android Apps for Semantics-Aware Malware Detection

IF 6.3 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Xin Zhao;Mu Zhang;Xiaopeng Ke;Yu Pan;Yue Duan;Sheng Zhong;Fengyuan Xu
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引用次数: 0

Abstract

The emerging virtual machine-based Android packers render existing unpacking techniques ineffective. The state-of-the-art unpacker falls short because it relies on unreliable heuristics and manually crafted semantic models. Hence, it cannot precisely recover app semantics necessary for malware detection. In this paper, we propose DeepVMUnProtect, a deep learning-based approach to automatically and accurately capture the semantics of VM-packed code, so as to facilitate semantic-based Android malware classification. Experiments have shown that DeepVMUnProtect outperforms the state-of-the-art tool on recovering opcode semantics in Qihoo(58.3%), Baidu(47.5%) and NMMP (58.8%) respectively, and can enable semantics-aware malware detection which prior work fails to do.
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来源期刊
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security 工程技术-工程:电子与电气
CiteScore
14.40
自引率
7.40%
发文量
234
审稿时长
6.5 months
期刊介绍: The IEEE Transactions on Information Forensics and Security covers the sciences, technologies, and applications relating to information forensics, information security, biometrics, surveillance and systems applications that incorporate these features
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