Stealth attacks: An extended insight into the obfuscation effects on Android malware

被引:110
作者
Maiorca, Davide [1 ]
Ariu, Davide [1 ]
Corona, Igino [1 ]
Aresu, Marco [1 ]
Giacinto, Giorgio [1 ]
机构
[1] Univ Cagliari, Dept Elect & Elect Engn, I-09123 Cagliari, Italy
关键词
Android; Malware; Obfuscation; Evasion; DexGuard; Dalvik; Entry points; Signatures; Strings; Bytecode;
D O I
10.1016/j.cose.2015.02.007
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order to effectively evade anti-malware solutions, Android malware authors are progressively resorting to automatic obfuscation strategies. Recent works have shown, on small-scale experiments, the possibility of evading anti-malware engines by applying simple obfuscation transformations on previously detected malware samples. In this paper, we provide a large-scale experiment in which the detection performances of a high number of anti-malware solutions are tested against two different sets of malware samples that have been obfuscated according to different strategies. Moreover, we show that anti-malware engines search for possible malicious content inside assets and entry-point classes. We also provide a temporal analysis of the detection performances of anti-malware engines to verify if their resilience has improved since 2013. Finally, we show how, by manipulating the area of the Android executable that contains the strings used by the application, it is possible to deceive anti-malware engines so that they will identify legitimate samples as malware. On one hand, the attained results show that anti-malware systems have improved their resilience against trivial obfuscation techniques. On the other hand, more complex changes to the application executable have proved to be still effective against detection. Thus, we claim that a deeper static (or dynamic) analysis of the application is needed to improve the robustness of such systems. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:16 / 31
页数:16
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  • [22] An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks
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    Bala, Neha
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    Boult, Terrance E.
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  • [26] Robust Android Malware Detection System Against Adversarial Attacks Using Q-Learning
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    Sanjay K. Sahay
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    Sahay, Sanjay K.
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    [J]. INFORMATION SYSTEMS FRONTIERS, 2021, 23 (04) : 867 - 882
  • [28] MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version)
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  • [29] Adversarial superiority in android malware detection: Lessons from reinforcement learning based evasion attacks and defenses
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    Nandanwar, Adarsh
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