Understanding Android security

This paper details a survey of Android users in an attempt to shed light on how users perceive the risks associated with app permissions and in- built adware. A series of questions was presented in a Web survey, with results suggesting interesting differences between males and females in installation be- haviour and attitudes toward security

The Paradox of Choice: Investigating Selection Strategies for Android Malware Datasets Using a Machine-learning Approach

The increase in the number of mobile devices that use the Android operating system has attracted the attention of cybercriminals who want to disrupt or gain unauthorized access to them through malware infections. To prevent such malware, cybersecurity experts and researchers require datasets of malware samples that most available antivirus software programs cannot detect. However, researchers have infrequently discussed how to identify evolving Android malware characteristics from different sources. In this paper, we analyze a wide variety of Android malware datasets to determine more discriminative features such as permissions and intents. We then apply machine-learning techniques on collected samples of different datasets based on the acquired features’ similarity. We perform random sampling on each cluster of collected datasets to check the antivirus software’s capability to detect the sample. We also discuss some common pitfalls in selecting datasets. Our findings benefit firms by acting as an exhaustive source of information about leading Android malware datasets

Detecting Targeted Smartphone Malware with Behavior-Triggering Stochastic Models

none4sinoneGuillermo Suarez-Tangil; Mauro Conti; Juan E. Tapiador; and Pedro Peris-LopezGuillermo Suarez, Tangil; Conti, Mauro; Juan E., Tapiador; Pedro Peris, Lope

TriFlow: Triaging Android Applications using Speculative Information Flows

Information 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.This work was supported by the MINECO grants TIN2013-46469-R and TIN2016-79095-C2-2-R, and by the CAM grant S2013/ICE-3095