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Malware detection techniques achieve great success with deeper insight into the semantics of malware. Among existing detection techniques, function call graph (FCG) based methods achieve promising performance due to their prominent representations of malware's functionalities. Meanwhile, recent adversarial attacks not only perturb feature vectors to deceive classifiers (i.e., feature-space attacks) but also investigate how to generate real evasive malware (i.e., problem-space attacks). However, existing problem-space attacks are limited due to their inconsistent transformations between feature space and problem space.

In this paper, we propose the first structural attack against graph-based Android malware detection techniques, which addresses the inverse-transformation problem [1] between feature-space attacks and problem-space attacks. We design a Heuristic optimization model integrated with …