Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

By Xiao Chen, Chaoran Li, Derui Wang, Sheng Wen, Jun Zhang, Surya Nepal, Yang Xiang, K. Ren. IEEE Transactions on Information Forensics and Security, 2018

296 citations recorded by the source corpus.

Machine learning-based solutions have been successfully employed for the 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.

Subfields: Artificial Intelligence, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/843c1f0d1721f513cf91054a9321978536d5bd5b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qing-Long Han
2. Jun Zhang
3. Sheng Wen
4. Wei Luo
5. Surya Nepal
6. Guanjun Lin
7. Lei Pan
8. Junyang Qiu

In Software

1. Sheng Wen
2. Qing-Long Han
3. Surya Nepal
4. Guanjun Lin
5. Jun Zhang
6. Yang Xiang

In Signal Processing

1. Sheng Wen
2. Wei Luo
3. Surya Nepal
4. Lei Pan
5. Junyang Qiu
6. Jun Zhang
7. Yang Xiang

Citations in the corpus

• Software Vulnerability Detection Using Deep Neural Networks: A Survey (2020, background): “[15] X.”
• A Survey of Android Malware Detection with Deep Neural Models (2020, methodology): “Currently, traditional ML or DL intelligence has been successfully used to produce new and evasive Android malware variants [136].”