DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware

TitleDroidSieve: Fast and Accurate Classification of Obfuscated Android Malware
Publication TypeConference Paper
Year of Publication2017
AuthorsSuarez-Tangil, G, Dash, SKumar, Ahmadi, M, Kinder, J, Giacinto, G, Cavallaro, L
Conference NameProceedings of the Seventh ACM on Conference on Data and Application Security and Privacy
PublisherAssociation for Computing Machinery
Conference LocationNew York, NY, USA
ISBN Number9781450345231
Keywordsandroid malware detection, Classification, Machine Learning, malware family identification, native code, obfuscation, scalability, security

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.DroidSieve exploits obfuscation-invariant features and artifacts introduced by obfuscation mechanisms used in malware. At the same time, these purely static features are designed for processing at scale and can be extracted quickly.For malware detection, we achieve up to 99.82% accuracy with zero false positives; for family identification of obfuscated malware, we achieve 99.26% accuracy at a fraction of the computational cost of state-of-the-art techniques.

Citation Key10.1145/3029806.3029825