adminweb/ February 12, 2020/ Technology/ 0 comments

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 example scan 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 byte code rather than manifest, existing attacking/defending methods are no longer effective.

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