6,226 research outputs found
The Transitivity of Trust Problem in the Interaction of Android Applications
Mobile phones have developed into complex platforms with large numbers of
installed applications and a wide range of sensitive data. Application security
policies limit the permissions of each installed application. As applications
may interact, restricting single applications may create a false sense of
security for the end users while data may still leave the mobile phone through
other applications. Instead, the information flow needs to be policed for the
composite system of applications in a transparent and usable manner. In this
paper, we propose to employ static analysis based on the software architecture
and focused data flow analysis to scalably detect information flows between
components. Specifically, we aim to reveal transitivity of trust problems in
multi-component mobile platforms. We demonstrate the feasibility of our
approach with Android applications, although the generalization of the analysis
to similar composition-based architectures, such as Service-oriented
Architecture, can also be explored in the future
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
AdSplit: Separating smartphone advertising from applications
A wide variety of smartphone applications today rely on third-party
advertising services, which provide libraries that are linked into the hosting
application. This situation is undesirable for both the application author and
the advertiser. Advertising libraries require additional permissions, resulting
in additional permission requests to users. Likewise, a malicious application
could simulate the behavior of the advertising library, forging the user's
interaction and effectively stealing money from the advertiser. This paper
describes AdSplit, where we extended Android to allow an application and its
advertising to run as separate processes, under separate user-ids, eliminating
the need for applications to request permissions on behalf of their advertising
libraries.
We also leverage mechanisms from Quire to allow the remote server to validate
the authenticity of client-side behavior. In this paper, we quantify the degree
of permission bloat caused by advertising, with a study of thousands of
downloaded apps. AdSplit automatically recompiles apps to extract their ad
services, and we measure minimal runtime overhead. We also observe that most ad
libraries just embed an HTML widget within and describe how AdSplit can be
designed with this in mind to avoid any need for ads to have native code
Automatically Securing Permission-Based Software by Reducing the Attack Surface: An Application to Android
A common security architecture, called the permission-based security model
(used e.g. in Android and Blackberry), entails intrinsic risks. For instance,
applications can be granted more permissions than they actually need, what we
call a "permission gap". Malware can leverage the unused permissions for
achieving their malicious goals, for instance using code injection. In this
paper, we present an approach to detecting permission gaps using static
analysis. Our prototype implementation in the context of Android shows that the
static analysis must take into account a significant amount of
platform-specific knowledge. Using our tool on two datasets of Android
applications, we found out that a non negligible part of applications suffers
from permission gaps, i.e. does not use all the permissions they declare
Security Toolbox for Detecting Novel and Sophisticated Android Malware
This paper presents a demo of our Security Toolbox to detect novel malware in
Android apps. This Toolbox is developed through our recent research project
funded by the DARPA Automated Program Analysis for Cybersecurity (APAC)
project. The adversarial challenge ("Red") teams in the DARPA APAC program are
tasked with designing sophisticated malware to test the bounds of malware
detection technology being developed by the research and development ("Blue")
teams. Our research group, a Blue team in the DARPA APAC program, proposed a
"human-in-the-loop program analysis" approach to detect malware given the
source or Java bytecode for an Android app. Our malware detection apparatus
consists of two components: a general-purpose program analysis platform called
Atlas, and a Security Toolbox built on the Atlas platform. This paper describes
the major design goals, the Toolbox components to achieve the goals, and the
workflow for auditing Android apps. The accompanying video
(http://youtu.be/WhcoAX3HiNU) illustrates features of the Toolbox through a
live audit.Comment: 4 pages, 1 listing, 2 figure
Static Analysis for Extracting Permission Checks of a Large Scale Framework: The Challenges And Solutions for Analyzing Android
A common security architecture is based on the protection of certain
resources by permission checks (used e.g., in Android and Blackberry). It has
some limitations, for instance, when applications are granted more permissions
than they actually need, which facilitates all kinds of malicious usage (e.g.,
through code injection). The analysis of permission-based framework requires a
precise mapping between API methods of the framework and the permissions they
require. In this paper, we show that naive static analysis fails miserably when
applied with off-the-shelf components on the Android framework. We then present
an advanced class-hierarchy and field-sensitive set of analyses to extract this
mapping. Those static analyses are capable of analyzing the Android framework.
They use novel domain specific optimizations dedicated to Android.Comment: IEEE Transactions on Software Engineering (2014). arXiv admin note:
substantial text overlap with arXiv:1206.582
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