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In this paper, we propose the concept of attack scenarios, which can be learned and selected from a set of malicious applications and described by sets of Android APIs, to characterize Android malware. Because of its characteristics that produce almost no false-positive, attack scenarios can be used as a pre-filter for machine-learning based detectors to enhance the detection performance at low false-positive...
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