10,285 research outputs found
Analyzing the User Interface of Android Apps
When interacting with Android apps, users may not always get what they expect. For instance, when clicking on a button labeled “upload picture”, the app may actually leak the user location while uploading photos to a cloud service. In this paper we present BACKSTAGE, a static analysis framework that binds UI elements to their corresponding callbacks, and further extracts actions, in the form of Android sensitive API calls, that may be triggered by events on such UI elements. We illustrate how the analysis implemented by BACKSTAGE works, and we compare it with similar frameworks
I know what leaked in your pocket: uncovering privacy leaks on Android Apps with Static Taint Analysis
Android applications may leak privacy data carelessly or maliciously. In this
work we perform inter-component data-flow analysis to detect privacy leaks
between components of Android applications. Unlike all current approaches, our
tool, called IccTA, propagates the context between the components, which
improves the precision of the analysis. IccTA outperforms all other available
tools by reaching a precision of 95.0% and a recall of 82.6% on DroidBench. Our
approach detects 147 inter-component based privacy leaks in 14 applications in
a set of 3000 real-world applications with a precision of 88.4%. With the help
of ApkCombiner, our approach is able to detect inter-app based privacy leaks
ACMiner: Extraction and Analysis of Authorization Checks in Android's Middleware
Billions of users rely on the security of the Android platform to protect
phones, tablets, and many different types of consumer electronics. While
Android's permission model is well studied, the enforcement of the protection
policy has received relatively little attention. Much of this enforcement is
spread across system services, taking the form of hard-coded checks within
their implementations. In this paper, we propose Authorization Check Miner
(ACMiner), a framework for evaluating the correctness of Android's access
control enforcement through consistency analysis of authorization checks.
ACMiner combines program and text analysis techniques to generate a rich set of
authorization checks, mines the corresponding protection policy for each
service entry point, and uses association rule mining at a service granularity
to identify inconsistencies that may correspond to vulnerabilities. We used
ACMiner to study the AOSP version of Android 7.1.1 to identify 28
vulnerabilities relating to missing authorization checks. In doing so, we
demonstrate ACMiner's ability to help domain experts process thousands of
authorization checks scattered across millions of lines of code
Sound and Precise Malware Analysis for Android via Pushdown Reachability and Entry-Point Saturation
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
StoryDroid: Automated Generation of Storyboard for Android Apps
Mobile apps are now ubiquitous. Before developing a new app, the development
team usually endeavors painstaking efforts to review many existing apps with
similar purposes. The review process is crucial in the sense that it reduces
market risks and provides inspiration for app development. However, manual
exploration of hundreds of existing apps by different roles (e.g., product
manager, UI/UX designer, developer) in a development team can be ineffective.
For example, it is difficult to completely explore all the functionalities of
the app in a short period of time. Inspired by the conception of storyboard in
movie production, we propose a system, StoryDroid, to automatically generate
the storyboard for Android apps, and assist different roles to review apps
efficiently. Specifically, StoryDroid extracts the activity transition graph
and leverages static analysis techniques to render UI pages to visualize the
storyboard with the rendered pages. The mapping relations between UI pages and
the corresponding implementation code (e.g., layout code, activity code, and
method hierarchy) are also provided to users. Our comprehensive experiments
unveil that StoryDroid is effective and indeed useful to assist app
development. The outputs of StoryDroid enable several potential applications,
such as the recommendation of UI design and layout code
Keeping Context In Mind: Automating Mobile App Access Control with User Interface Inspection
Recent studies observe that app foreground is the most striking component
that influences the access control decisions in mobile platform, as users tend
to deny permission requests lacking visible evidence. However, none of the
existing permission models provides a systematic approach that can
automatically answer the question: Is the resource access indicated by app
foreground? In this work, we present the design, implementation, and evaluation
of COSMOS, a context-aware mediation system that bridges the semantic gap
between foreground interaction and background access, in order to protect
system integrity and user privacy. Specifically, COSMOS learns from a large set
of apps with similar functionalities and user interfaces to construct generic
models that detect the outliers at runtime. It can be further customized to
satisfy specific user privacy preference by continuously evolving with user
decisions. Experiments show that COSMOS achieves both high precision and high
recall in detecting malicious requests. We also demonstrate the effectiveness
of COSMOS in capturing specific user preferences using the decisions collected
from 24 users and illustrate that COSMOS can be easily deployed on smartphones
as a real-time guard with a very low performance overhead.Comment: Accepted for publication in IEEE INFOCOM'201
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