journal article·2022-05-14Open access

Evaluation of Survivability of the Automatically Obfuscated Android Malware

Himanshu Patel, Deep Patel, Jaspreet Ahluwalia, Vaishali Kapoor, Karthik Narasimhan, Harmanpreet Singh, Harmanjot Kaur, Gadi Harshitha Reddy, Sai Sushma Peruboina, Sergey Butakov

Applied Sciences· Volume 12· Issue 10

Abstract

Malware is a growing threat to all mobile platforms and hundreds of new malicious applications are being detected every day. At the same time, the development of automated software obfuscation techniques allows for the easy production of new malware variants even by attackers with entry-level programming skills. Such obfuscation techniques can evade the signature-based mechanism implemented in current antimalware technology. This paper presents the results of a study that examined how automated obfuscation techniques affect malicious and benign applications by two widely used malware detection approaches, namely static and dynamic analyses. The research explored 5000 samples of malware and benign programs and evaluated the impact of automated obfuscation on Android applications. The experimental results indicated that (1) up to 73% of the reviewed applications “survived” the automated obfuscation; (2) automated obfuscation reduced the detection ratio to 65–85% depending on the obfuscation method used. These findings call for a more active use of advanced malware detection methods in commonly used antivirus platforms.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Malware Detection Techniques · Software Testing and Debugging Techniques · Network Security and Intrusion Detection

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Information Systems Security and Assurance Management, Concordia University of Edmonton, Edmonton, AB T5B 4E4, Canada

Funding

Concordia University of Edmonton

References (32)

Show source-provided references
1. Statcounter Global Stats (2021, June 12). Mobile Operating System Market Share Worldwide. Available online: https://gs.statcounter.com/os-market-share/mobile/worldwide.
2. Different obfuscation techniques for code protection10.1016/j.procs.2015.10.114 ↗
3. Collberg, C., Myles, G.R., and Huntwork, A. (2003). Sandmark—A Tool for Software Protection Research, IEEE Security & Privacy.10.1109/MSECP.2003.1219058 ↗
4. Berzinskas, L. (2021, May 13). Obfuscating Android Apps: Do You Know Your Choices for Protection?. Available online: https://proandroiddev.com/obfuscation-is-important-do-you-know-your-options-30b3ef396dfe.
5. Obfuscapk: An open-source black-box obfuscation tool for Android apps10.1016/j.softx.2020.100403 ↗
6. Google (2021, May 13). Android Architecture Components. Available online: https://developer.android.com/topic/libraries/architecture.
7. Android Developers (2021, May 14). Application Fundamentals. Available online: https://developer.android.com/guide/components/fundamentals.
8. Google (2021, May 20). App Manifest Overview. Available online: https://developer.android.com/guide/topics/manifest/manifest-intro.
9. Android Developers (2021, May 20). App Manifest Overview. Available online: https://developer.android.com/guide/topics/manifest/manifest-intro#components.
10. Kotipall, S.R., and Imran, M. (2016). Understanding the app’s attack surface. Hacking Android, Packt.
11. Codemagic (2021, May 23). Android Code Signing. Available online: https://docs.codemagic.io/code-signing/android-code-signing/.
12. Dynamic Malware Analysis in the Modern Era—A State of the Art Survey
13. Implementation of malware analysis using static and dynamic analysis method
14. Bakour, K., Unver, H.M., and Ghanem, R. (2018, January 20–23). The Android Malware Static Analysis: Techniques, Limitations, and Open Challenges. Proceedings of the 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, Bosnia and Herzegovina.10.1109/UBMK.2018.8566573 ↗
15. MEGDroid: A model-driven event generation framework for dynamic android malware analysis10.1016/j.infsof.2021.106569 ↗
16. Catch Me If You Can: Evaluating Android Anti-Malware Against Transformation Attacks10.1109/TIFS.2013.2290431 ↗
17. Hammad, M., Garcia, J., and Malek, S. (June, January 27). A large-scale empirical study on the effects of code obfuscations on Android apps and anti-malware products. Proceedings of the 40th International Conference on Software Engineering, New York, NY, USA.
18. Ajiri, V., Butakov, S., and Zavarsky, P. (2020, January 25–27). Detection Efficiency of Static Analysers against obfuscated Android Malware. Proceedings of the IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), Baltimore, MD, USA.10.1109/BigDataSecurity-HPSC-IDS49724.2020.00049 ↗
19. System Call Analysis of Android Malware Families10.17485/ijst/2016/v9i21/90273 ↗
20. Wu, Y., Dou, S., Zou, D., Yang, W., Qiang, W., and Jin, H. (2021). Obfuscation-resilient Android Malware Analysis Based on Contrastive Learning. arXiv.
21. A survey of android application and malware hardening10.1016/j.cosrev.2021.100365 ↗
22. Allix, K., Bissyandé, T.F., Klein, J., and Traon, Y.L. (2016, January 14–22). AndroZoo: Collecting Millions of Android Apps for the Research Community. Proceedings of the IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR), Austin, TX, USA.10.1145/2901739.2903508 ↗
23. System Calls Analysis of Malwares on Android
24. Google (2021, June 13). Manifest.Permission. Available online: https://developer.android.com/reference/android/Manifest.permission.
25. Virus, T. (2021, March 22). VirusTotal API Version 3 Overview. Available online: https://developers.virustotal.com/v3.0/reference#overview.
26. Yuan, H., Tang, Y., Sun, W., and Liu, L. (2020). A detection method for android application security based on TF-IDF and machine learning. PLoS ONE, 15.10.1371/journal.pone.0238694 ↗
27. Google Devloper (2021, June 17). Run Apps on the Android Emulator. Available online: https://developer.android.com/studio/run/emulator.
28. Idanr (2021, March 22). CuckooDroid—Automated Android Malware Analysis. Available online: https://github.com/idanr1986/cuckoo-droid.
29. Patel, D. (2021, June 17). Evaluation of obfuscated android malware. Available online: https://github.com/ddeepp109/Android-Malware-Analysis.
30. (2021, March 24). Quark-Engine. Available online: https://github.com/quark-engine/quark-engine.
31. Willadsen, K. (2021, January 12). Meld. Available online: https://meldmerge.org/.
32. Google (2020, February 19). Request App Permissions, Available online: https://developer.android.com/training/permissions/requesting.