Titolo:  DroidSieve: Fast and accurate classification of obfuscated android malware
Autori: 
Data di pubblicazione:  2017
Autori:  Suarez Tangil, Guillermo; Dash, Santanu Kumar; Ahmadi, Mansour; Kinder, Johannes; Giacinto, Giorgio; Cavallaro, Lorenzo
Presenza coautori internazionali: 
Lingua:  Inglese
Titolo del libro:  CODASPY 2017 - Proceedings of the 7th ACM Conference on Data and Application Security and Privacy
ISBN:  9781450345231
Editore:  Association for Computing Machinery
Pagina iniziale:  309
Pagina finale:  320
Numero di pagine:  12
Digital Object Identifier (DOI):  http://dx.doi.org/10.1145/3029806.3029825
Codice identificativo Scopus:  2-s2.0-85018485321
Revisione (peer review):  Esperti anonimi
Nome del convegno:  7th ACM Conference on Data and Application Security and Privacy, CODASPY 2017
Periodo del convegno:  22-24 March 2017
Luogo del convegno:  Scottsdale, Arizona, USA
Abstract:  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.
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