The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
Open accessAbstract
Machine learning based solutions have been successfully employed for automatic detection of malware on Android. However, machine learning models lack robustness to adversarial examples, which are crafted by adding carefully chosen perturbations to the normal inputs. So far, the adversarial examples can only deceive detectors that rely on syntactic features (e.g., requested permissions, API calls, etc), and the perturbations can only be implemented by simply modifying application's manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new attacking method that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK that can successfully deceive the machine learning detectors. We develop an automated tool to generate the adversarial examples without human intervention. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK's Dalvik bytecode rather than Android manifest to evade from recent detectors. We demonstrate our attack on two state-of-the-art Android malware detection schemes, MaMaDroid and Drebin. Our results show that the malware detection rates decreased from 96% to 0% in MaMaDroid, and from 97% to 0% in Drebin, with just a small number of codes to be inserted into the APK.
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Cites
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Self-citationSecurity and privacy in 6G networks: New areas and new challenges
Wang, M., Zhu, T., Zhang, T., Zhang, J., Yu, S., & Zhou, W. (2020) · Digital Communications and Networks · doi:10.1016/j.dcan.2020.07.003 · Cited by 363
Self-citationDeng, Z., Sun, R., Xue, M., Ma, W., Wen, S., Nepal, S., & Xiang, Y. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3581103 · Cited by 36
Self-citationInsider threat detection using supervised machine learning algorithms
Manoharan, P., Yin, J., Wang, H., Zhang, Y., & Ye, W. (2024) · Telecommunication Systems · doi:10.1007/s11235-023-01085-3 · Cited by 34
Insider Threat Identification Using the Simultaneous Neural Learning of Multi-Source Logs
Liu, L., Chen, C., Zhang, J., De Vel, O., & Xiang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2957055 · Cited by 31
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Metadata
- Authors
- Chen, X., Li, C., Wang, D., Wen, S., Zhang, J., Nepal, S., Xiang, Y., & Ren, K.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2020
- Volume
- 15
- Pages
- 987-1001
- ISSN
- 2331-8422 (ISSN-L)
- 1556-6013
- 1556-6021
- OpenAlex
- W2966342255
- Cited by
- 328
- References
- 37
- Type
- posted content
- Open access
- green
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- Chen, X., Li, C., Wang, D., Wen, S., Zhang, J., Nepal, S., Xiang, Y., & Ren, K. (2020). Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/tifs.2019.2932228
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