  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.

Cryptography and Security