2017 · 203 citations · 30 references
ComputingComputer ScienceCybersecurityMachine LearningMalware AnalysisMobile SecurityCode Obfuscation AnalysisMalware ClassificationMalware IntelligenceMalware Reverse EngineeringMobile Application DevelopmentMobile Application Reverse EngineeringAndroid Malware ClassificationMachine Learning ClassificationMachine Learning For Malware ClassificationMachine Learning-based ClassificationMalicious App ClassificationMalicious App DetectionMobile Malware DetectionSyntactic Features
With more than two million applications, Android marketplaces require automatic and scalable methods to efficiently vet apps for the absence of malicious threats. Recent techniques have successfully relied on the extraction of lightweight syntactic features suitable for machine learning classification, but despite their promising results, the very nature of such features suggest they would unlikely--on their own--be suitable for detecting obfuscated Android malware. To address this challenge, we propose DroidSieve, an Android malware classifier based on static analysis that is fast, accurate, and resilient to obfuscation. For a given app, DroidSieve first decides whether the app is malicious and, if so, classifies it as belonging to a family of related malware.
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