1,569 research outputs found
SafeWeb: A Middleware for Securing Ruby-Based Web Applications
Web applications in many domains such as healthcare and finance must process sensitive data, while complying with legal policies regarding the release of different classes of data to different parties. Currently, software bugs may lead to irreversible disclosure of confidential data in multi-tier web applications. An open challenge is how developers can guarantee these web applications only ever release sensitive data to authorised users without costly, recurring security audits.
Our solution is to provide a trusted middleware that acts as a “safety net” to event-based enterprise web applications by preventing harmful data disclosure before it happens. We describe the design and implementation of SafeWeb, a Ruby-based middleware that associates data with security labels and transparently tracks their propagation at different granularities across a multi-tier web architecture with storage and complex event processing. For efficiency, maintainability and ease-of-use, SafeWeb exploits the dynamic features of the Ruby programming language to achieve label propagation and data flow enforcement. We evaluate SafeWeb by reporting our experience of implementing a web-based cancer treatment application and deploying it as part of the UK National Health Service (NHS)
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
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
Preventing SQL Injection through Automatic Query Sanitization with ASSIST
Web applications are becoming an essential part of our everyday lives. Many
of our activities are dependent on the functionality and security of these
applications. As the scale of these applications grows, injection
vulnerabilities such as SQL injection are major security challenges for
developers today. This paper presents the technique of automatic query
sanitization to automatically remove SQL injection vulnerabilities in code. In
our technique, a combination of static analysis and program transformation are
used to automatically instrument web applications with sanitization code. We
have implemented this technique in a tool named ASSIST (Automatic and Static
SQL Injection Sanitization Tool) for protecting Java-based web applications.
Our experimental evaluation showed that our technique is effective against SQL
injection vulnerabilities and has a low overhead.Comment: In Proceedings TAV-WEB 2010, arXiv:1009.330
MobileAppScrutinator: A Simple yet Efficient Dynamic Analysis Approach for Detecting Privacy Leaks across Mobile OSs
Smartphones, the devices we carry everywhere with us, are being heavily
tracked and have undoubtedly become a major threat to our privacy. As "tracking
the trackers" has become a necessity, various static and dynamic analysis tools
have been developed in the past. However, today, we still lack suitable tools
to detect, measure and compare the ongoing tracking across mobile OSs. To this
end, we propose MobileAppScrutinator, based on a simple yet efficient dynamic
analysis approach, that works on both Android and iOS (the two most popular OSs
today). To demonstrate the current trend in tracking, we select 140 most
representative Apps available on both Android and iOS AppStores and test them
with MobileAppScrutinator. In fact, choosing the same set of apps on both
Android and iOS also enables us to compare the ongoing tracking on these two
OSs. Finally, we also discuss the effectiveness of privacy safeguards available
on Android and iOS. We show that neither Android nor iOS privacy safeguards in
their present state are completely satisfying
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