979 research outputs found
Longitudinal Analysis of Android Ad Library Permissions
This paper investigates changes over time in the behavior of Android ad
libraries. Taking a sample of 100,000 apps, we extract and classify the ad
libraries. By considering the release dates of the applications that use a
specific ad library version, we estimate the release date for the library, and
thus build a chronological map of the permissions used by various ad libraries
over time. We find that the use of most permissions has increased over the last
several years, and that more libraries are able to use permissions that pose
particular risks to user privacy and security.Comment: Most 201
Longitudinal performance analysis of machine learning based Android malware detectors
This paper presents a longitudinal study of the performance of machine learning classifiers for Android malware detection. The study is undertaken using features extracted from Android applications first seen between 2012 and 2016. The aim is to investigate the extent of performance decay over time for various machine learning classifiers trained with static features extracted from date-labelled benign and malware application sets. Using date-labelled apps allows for true mimicking of zero-day testing, thus providing a more realistic view of performance than the conventional methods of evaluation that do not take date of appearance into account. In this study, all the investigated machine learning classifiers showed progressive diminishing performance when tested on sets of samples from a later time period. Overall, it was found that false positive rate (misclassifying benign samples as malicious) increased more substantially compared to the fall in True Positive rate (correct classification of malicious apps) when older models were tested on newer app samples
Comprehension of Ads-supported and Paid Android Applications: Are They Different?
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
Characterizing Location-based Mobile Tracking in Mobile Ad Networks
Mobile apps nowadays are often packaged with third-party ad libraries to
monetize user data
Third Party Tracking in the Mobile Ecosystem
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
- …