作者: Xuxian Jiang , Yajin Zhou , Wu Zhou , Zhi Wang
DOI:
关键词: Internet privacy 、 Android (operating system) 、 Computer security 、 Android security 、 Heuristics 、 Infection rate 、 Malware 、 Android malware 、 Computer science 、 Mobile malware
摘要: In this paper, we present a systematic study for the detection of malicious applications (or apps) on popular Android Markets. To end, first propose permissionbased behavioral footprinting scheme to detect new samples known malware families. Then apply heuristics-based filtering identify certain inherent behaviors unknown We implemented both schemes in system called DroidRanger. The experiments with 204, 040 apps collected from five different Markets May-June 2011 reveal 211 ones: 32 official Market (0.02% infection rate) and 179 alternative marketplaces (infection rates ranging 0.20% 0.47%). Among those apps, our also uncovered two zero-day (in 40 apps): one other marketplaces. results show that current are functional relatively healthy. However, there is clear need rigorous policing process, especially non-regulated