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Android malware is a spreading disease in the virtual world. Anti-virus anddetection systems continuously undergo patches and updates to defend againstthese threats. Most of the latest approaches in malware detection use MachineLearning (ML). Against the robustifying effort of detection systems, raise the\emph{evasion attacks}, where an adversary changes its targeted samples so thatthey are misclassified as benign. This paper considers two kinds of evasionattacks: feature-space and problem-space. \emph{Feature-space} attacks consideran adversary who manipulates ML features to evade the correct classificationwhile minimizing or constraining the total manipulations.\textit{Problem-space} attacks refer to evasion attacks that change the actualsample. Specifically, this paper analyzes the gap between these two types inthe Android malware domain. The gap between the two types of evasion attacks isexamined via the retraining process of classifiers using each one of theevasion attack types. The experiments show that the gap between these two typesof retrained classifiers is dramatic and may increase to 96\%. Retrainedclassifiers of feature-space evasion attacks have been found to be either lesseffective or completely ineffective against problem-space evasion attacks.Additionally, exploration of different problem-space evasion attacks shows thatretraining of one problem-space evasion attack may be effective against otherproblem-space evasion attacks.

Do You Think You Can Hold Me? The Real Challenge of Problem-Space Evasion Attacks