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dc.contributor.authorShen, Zong-Xianen_US
dc.contributor.authorHsu, Chia-Weien_US
dc.contributor.authorShieh, Shiuhpyng Winstonen_US
dc.date.accessioned2018-08-21T05:52:42Z-
dc.date.available2018-08-21T05:52:42Z-
dc.date.issued2017-11-01en_US
dc.identifier.issn1536-1233en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TMC.2017.2690425en_US
dc.identifier.urihttp://hdl.handle.net/11536/143875-
dc.description.abstractThe prevalence of Android platform has attracted adversaries to craft malicious payloads for illegal profit. Such malicious artifacts are frequently reused and embedded in benign, paid apps to lure victims that the apps have been cracked for free. To discover these fraudulent apps, administrators of app markets desire an automated scanning process to maintain the health of app ecosystem. However, conventional approaches cannot be efficiently applied due to the lack of a scalable, effective approach to malware characteristics aggregation. On the other hand, the vast number of apps significantly increases the analysis complexity. In this paper, we propose Petridish which generates discriminative models against the repacked malicious apps. These representative models of malicious semantics can be progressively distilled with malign and benign samples. These models can further detect repacked malicious apps. Our experiment shows that, after two retraining rounds, Petridish achieved an average of 28 percent progressive detection improvement from 63 to 91.2 percent for the large families, exceeding 38 test samples in size. With noise reduction, it accomplished 88 percent detection rate and 1.7 percent false alarm rate. The characteristics aggregation approach will become critical in the age of app explosion.en_US
dc.language.isoen_USen_US
dc.subjectRepackaged appsen_US
dc.subjectmalwareen_US
dc.subjectAndroiden_US
dc.titleSecurity Semantics Modeling with Progressive Distillationen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TMC.2017.2690425en_US
dc.identifier.journalIEEE TRANSACTIONS ON MOBILE COMPUTINGen_US
dc.citation.volume16en_US
dc.citation.spage3196en_US
dc.citation.epage3208en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000412231100016en_US
Appears in Collections:Articles