Mirzaei, OmidSuarez-Tangil, GuillermoEstévez Tapiador, Juan ManuelFuentes García-Romero de Tejada, José María de2018-03-152018-03-152017-04-02Ramesh Karri, Ozgur Sinanoglu, Ahmad-Reza Sadeghi, Xun Yi. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, AsiaCCS 2017, Abu Dhabi, United Arab Emirates, April 2-6, 2017. ACM, 640-651978-1-4503-4944-4https://hdl.handle.net/10016/26107Information flows in Android can be effectively used to give an informative summary of an application’s behavior, showing how and for what purpose apps use specific pieces of information. This has been shown to be extremely useful to characterize risky behaviors and, ultimately, to identify unwanted or malicious applications in Android. However, identifying information flows in an application is computationally highly expensive and, with more than one million apps in the Google Play market, it is critical to prioritize applications that are likely to pose a risk. In this work, we develop a triage mechanism to rank applications considering their potential risk. Our approach, called TRIFLOW, relies on static features that are quick to obtain. TRIFLOW combines a probabilistic model to predict the existence of information flows with a metric of how significant a flow is in benign and malicious apps. Based on this, TRIFLOW provides a score for each application that can be used to prioritize analysis. TRIFLOW also provides an explanatory report of the associated risk. We evaluate our tool with a representative dataset of benign and malicious Android apps. Our results show that it can predict the presence of information flows very accurately and that the overall triage mechanism enables significant resource saving.application/pdfengACM New York, NY, USA ©2017Android securityMalware analysisInformation flowApp triageconference proceedingsInformáticahttp://dx.doi.org/10.1145/3052973.3053001open access640651Proceedings of the 2017 ACM on Asia Conference on Computer and Communications SecurityCC/0000027270