1,245 research outputs found

    Third Party Tracking in the Mobile Ecosystem

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    Third party tracking allows companies to identify users and track their behaviour across multiple digital services. This paper presents an empirical study of the prevalence of third-party trackers on 959,000 apps from the US and UK Google Play stores. We find that most apps contain third party tracking, and the distribution of trackers is long-tailed with several highly dominant trackers accounting for a large portion of the coverage. The extent of tracking also differs between categories of apps; in particular, news apps and apps targeted at children appear to be amongst the worst in terms of the number of third party trackers associated with them. Third party tracking is also revealed to be a highly trans-national phenomenon, with many trackers operating in jurisdictions outside the EU. Based on these findings, we draw out some significant legal compliance challenges facing the tracking industry.Comment: Corrected missing company info (Linkedin owned by Microsoft). Figures for Microsoft and Linkedin re-calculated and added to Table

    Android Malware Characterization using Metadata and Machine Learning Techniques

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    Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and meta-data to identify patterns in malware applications. Our experiments show that: (1) the permissions used by an application offer only moderate performance results; (2) other features publicly available at Android Markets are more relevant in detecting malware, such as the application developer and certificate issuer, and (3) compact and efficient classifiers can be constructed for the early detection of malware applications prior to code inspection or sandboxing.Comment: 4 figures, 2 tables and 8 page

    Comprehension of Ads-supported and Paid Android Applications: Are They Different?

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    The Android market is a place where developers offer paid and-or free apps to users. Free apps are interesting to users because they can try them immediately without incurring a monetary cost. However, free apps often have limited features and-or contain ads when compared to their paid counterparts. Thus, users may eventually need to pay to get additional features and-or remove ads. While paid apps have clear market values, their ads-supported versions are not entirely free because ads have an impact on performance. In this paper, first, we perform an exploratory study about ads-supported and paid apps to understand their differences in terms of implementation and development process. We analyze 40 Android apps and we observe that (i) ads-supported apps are preferred by users although paid apps have a better rating, (ii) developers do not usually offer a paid app without a corresponding free version, (iii) ads-supported apps usually have more releases and are released more often than their corresponding paid versions, (iv) there is no a clear strategy about the way developers set prices of paid apps, (v) paid apps do not usually include more functionalities than their corresponding ads-supported versions, (vi) developers do not always remove ad networks in paid versions of their ads-supported apps, and (vii) paid apps require less permissions than ads-supported apps. Second, we carry out an experimental study to compare the performance of ads-supported and paid apps and we propose four equations to estimate the cost of ads-supported apps. We obtain that (i) ads-supported apps use more resources than their corresponding paid versions with statistically significant differences and (ii) paid apps could be considered a most cost-effective choice for users because their cost can be amortized in a short period of time, depending on their usage.Comment: Accepted for publication in the proceedings of the IEEE International Conference on Program Comprehension 201

    Analysis of Bayesian classification-based approaches for Android malware detection

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware

    Sound and Precise Malware Analysis for Android via Pushdown Reachability and Entry-Point Saturation

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    We present Anadroid, a static malware analysis framework for Android apps. Anadroid exploits two techniques to soundly raise precision: (1) it uses a pushdown system to precisely model dynamically dispatched interprocedural and exception-driven control-flow; (2) it uses Entry-Point Saturation (EPS) to soundly approximate all possible interleavings of asynchronous entry points in Android applications. (It also integrates static taint-flow analysis and least permissions analysis to expand the class of malicious behaviors which it can catch.) Anadroid provides rich user interface support for human analysts which must ultimately rule on the "maliciousness" of a behavior. To demonstrate the effectiveness of Anadroid's malware analysis, we had teams of analysts analyze a challenge suite of 52 Android applications released as part of the Auto- mated Program Analysis for Cybersecurity (APAC) DARPA program. The first team analyzed the apps using a ver- sion of Anadroid that uses traditional (finite-state-machine-based) control-flow-analysis found in existing malware analysis tools; the second team analyzed the apps using a version of Anadroid that uses our enhanced pushdown-based control-flow-analysis. We measured machine analysis time, human analyst time, and their accuracy in flagging malicious applications. With pushdown analysis, we found statistically significant (p < 0.05) decreases in time: from 85 minutes per app to 35 minutes per app in human plus machine analysis time; and statistically significant (p < 0.05) increases in accuracy with the pushdown-driven analyzer: from 71% correct identification to 95% correct identification.Comment: Appears in 3rd Annual ACM CCS workshop on Security and Privacy in SmartPhones and Mobile Devices (SPSM'13), Berlin, Germany, 201
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