7,385 research outputs found
Target Directed Event Sequence Generation for Android Applications
Testing is a commonly used approach to ensure the quality of software, of
which model-based testing is a hot topic to test GUI programs such as Android
applications (apps). Existing approaches mainly either dynamically construct a
model that only contains the GUI information, or build a model in the view of
code that may fail to describe the changes of GUI widgets during runtime.
Besides, most of these models do not support back stack that is a particular
mechanism of Android. Therefore, this paper proposes a model LATTE that is
constructed dynamically with consideration of the view information in the
widgets as well as the back stack, to describe the transition between GUI
widgets. We also propose a label set to link the elements of the LATTE model to
program snippets. The user can define a subset of the label set as a target for
the testing requirements that need to cover some specific parts of the code. To
avoid the state explosion problem during model construction, we introduce a
definition "state similarity" to balance the model accuracy and analysis cost.
Based on this model, a target directed test generation method is presented to
generate event sequences to effectively cover the target. The experiments on
several real-world apps indicate that the generated test cases based on LATTE
can reach a high coverage, and with the model we can generate the event
sequences to cover a given target with short event sequences
Measuring third party tracker power across web and mobile
Third-party networks collect vast amounts of data about users via web sites
and mobile applications. Consolidations among tracker companies can
significantly increase their individual tracking capabilities, prompting
scrutiny by competition regulators. Traditional measures of market share, based
on revenue or sales, fail to represent the tracking capability of a tracker,
especially if it spans both web and mobile. This paper proposes a new approach
to measure the concentration of tracking capability, based on the reach of a
tracker on popular websites and apps. Our results reveal that tracker
prominence and parent-subsidiary relationships have significant impact on
accurately measuring concentration
FraudDroid: Automated Ad Fraud Detection for Android Apps
Although mobile ad frauds have been widespread, state-of-the-art approaches
in the literature have mainly focused on detecting the so-called static
placement frauds, where only a single UI state is involved and can be
identified based on static information such as the size or location of ad
views. Other types of fraud exist that involve multiple UI states and are
performed dynamically while users interact with the app. Such dynamic
interaction frauds, although now widely spread in apps, have not yet been
explored nor addressed in the literature. In this work, we investigate a wide
range of mobile ad frauds to provide a comprehensive taxonomy to the research
community. We then propose, FraudDroid, a novel hybrid approach to detect ad
frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI
state transition graphs and collects their associated runtime network traffics,
which are then leveraged to check against a set of heuristic-based rules for
identifying ad fraudulent behaviours. We show empirically that FraudDroid
detects ad frauds with a high precision (93%) and recall (92%). Experimental
results further show that FraudDroid is capable of detecting ad frauds across
the spectrum of fraud types. By analysing 12,000 ad-supported Android apps,
FraudDroid identified 335 cases of fraud associated with 20 ad networks that
are further confirmed to be true positive results and are shared with our
fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure
Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild
In this paper, we seek to better understand Android obfuscation and depict a
holistic view of the usage of obfuscation through a large-scale investigation
in the wild. In particular, we focus on four popular obfuscation approaches:
identifier renaming, string encryption, Java reflection, and packing. To obtain
the meaningful statistical results, we designed efficient and lightweight
detection models for each obfuscation technique and applied them to our massive
APK datasets (collected from Google Play, multiple third-party markets, and
malware databases). We have learned several interesting facts from the result.
For example, malware authors use string encryption more frequently, and more
apps on third-party markets than Google Play are packed. We are also interested
in the explanation of each finding. Therefore we carry out in-depth code
analysis on some Android apps after sampling. We believe our study will help
developers select the most suitable obfuscation approach, and in the meantime
help researchers improve code analysis systems in the right direction
Automatically Discovering, Reporting and Reproducing Android Application Crashes
Mobile developers face unique challenges when detecting and reporting crashes
in apps due to their prevailing GUI event-driven nature and additional sources
of inputs (e.g., sensor readings). To support developers in these tasks, we
introduce a novel, automated approach called CRASHSCOPE. This tool explores a
given Android app using systematic input generation, according to several
strategies informed by static and dynamic analyses, with the intrinsic goal of
triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented
crash report containing screenshots, detailed crash reproduction steps, the
captured exception stack trace, and a fully replayable script that
automatically reproduces the crash on a target device(s). We evaluated
CRASHSCOPE's effectiveness in discovering crashes as compared to five
state-of-the-art Android input generation tools on 61 applications. The results
demonstrate that CRASHSCOPE performs about as well as current tools for
detecting crashes and provides more detailed fault information. Additionally,
in a study analyzing eight real-world Android app crashes, we found that
CRASHSCOPE's reports are easily readable and allow for reliable reproduction of
crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on
Software Testing, Verification and Validation (ICST'16), Chicago, IL, April
10-15, 2016, pp. 33-4
Who you gonna call? Analyzing Web Requests in Android Applications
Relying on ubiquitous Internet connectivity, applications on mobile devices
frequently perform web requests during their execution. They fetch data for
users to interact with, invoke remote functionalities, or send user-generated
content or meta-data. These requests collectively reveal common practices of
mobile application development, like what external services are used and how,
and they point to possible negative effects like security and privacy
violations, or impacts on battery life. In this paper, we assess different ways
to analyze what web requests Android applications make. We start by presenting
dynamic data collected from running 20 randomly selected Android applications
and observing their network activity. Next, we present a static analysis tool,
Stringoid, that analyzes string concatenations in Android applications to
estimate constructed URL strings. Using Stringoid, we extract URLs from 30, 000
Android applications, and compare the performance with a simpler constant
extraction analysis. Finally, we present a discussion of the advantages and
limitations of dynamic and static analyses when extracting URLs, as we compare
the data extracted by Stringoid from the same 20 applications with the
dynamically collected data
Characterizing Location-based Mobile Tracking in Mobile Ad Networks
Mobile apps nowadays are often packaged with third-party ad libraries to
monetize user data
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