766 research outputs found
AndroShield:automated Android applications vulnerability detection, a hybrid static and dynamic analysis approach
The security of mobile applications has become a major research field which is associated with a lot of challenges. The high rate of developing mobile applications has resulted in less secure applications. This is due to what is called the “rush to release” as defined by Ponemon Institute. Security testing—which is considered one of the main phases of the development life cycle—is either not performed or given minimal time; hence, there is a need for security testing automation. One of the techniques used is Automated Vulnerability Detection. Vulnerability detection is one of the security tests that aims at pinpointing potential security leaks. Fixing those leaks results in protecting smart-phones and tablet mobile device users against attacks. This paper focuses on building a hybrid approach of static and dynamic analysis for detecting the vulnerabilities of Android applications. This approach is capsuled in a usable platform (web application) to make it easy to use for both public users and professional developers. Static analysis, on one hand, performs code analysis. It does not require running the application to detect vulnerabilities. Dynamic analysis, on the other hand, detects the vulnerabilities that are dependent on the run-time behaviour of the application and cannot be detected using static analysis. The model is evaluated against different applications with different security vulnerabilities. Compared with other detection platforms, our model detects information leaks as well as insecure network requests alongside other commonly detected flaws that harm users’ privacy. The code is available through a GitHub repository for public contribution
Do Android Taint Analysis Tools Keep Their Promises?
In recent years, researchers have developed a number of tools to conduct
taint analysis of Android applications. While all the respective papers aim at
providing a thorough empirical evaluation, comparability is hindered by varying
or unclear evaluation targets. Sometimes, the apps used for evaluation are not
precisely described. In other cases, authors use an established benchmark but
cover it only partially. In yet other cases, the evaluations differ in terms of
the data leaks searched for, or lack a ground truth to compare against. All
those limitations make it impossible to truly compare the tools based on those
published evaluations.
We thus present ReproDroid, a framework allowing the accurate comparison of
Android taint analysis tools. ReproDroid supports researchers in inferring the
ground truth for data leaks in apps, in automatically applying tools to
benchmarks, and in evaluating the obtained results. We use ReproDroid to
comparatively evaluate on equal grounds the six prominent taint analysis tools
Amandroid, DIALDroid, DidFail, DroidSafe, FlowDroid and IccTA. The results are
largely positive although four tools violate some promises concerning features
and accuracy. Finally, we contribute to the area of unbiased benchmarking with
a new and improved version of the open test suite DroidBench
ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic
It is well known that apps running on mobile devices extensively track and
leak users' personally identifiable information (PII); however, these users
have little visibility into PII leaked through the network traffic generated by
their devices, and have poor control over how, when and where that traffic is
sent and handled by third parties. In this paper, we present the design,
implementation, and evaluation of ReCon: a cross-platform system that reveals
PII leaks and gives users control over them without requiring any special
privileges or custom OSes. ReCon leverages machine learning to reveal potential
PII leaks by inspecting network traffic, and provides a visualization tool to
empower users with the ability to control these leaks via blocking or
substitution of PII. We evaluate ReCon's effectiveness with measurements from
controlled experiments using leaks from the 100 most popular iOS, Android, and
Windows Phone apps, and via an IRB-approved user study with 92 participants. We
show that ReCon is accurate, efficient, and identifies a wider range of PII
than previous approaches.Comment: Please use MobiSys version when referencing this work:
http://dl.acm.org/citation.cfm?id=2906392. 18 pages, recon.meddle.mob
Demystifying security and compatibility issues in Android Apps
Never before has any OS been so popular as Android. Existing mobile phones
are not simply devices for making phone calls and receiving SMS messages, but
powerful communication and entertainment platforms for web surfing, social
networking, etc. Even though the Android OS offers powerful communication and
application execution capabilities, it is riddled with defects (e.g., security
risks, and compatibility issues), new vulnerabilities come to light daily, and
bugs cost the economy tens of billions of dollars annually. For example,
malicious apps (e.g., back-doors, fraud apps, ransomware, spyware, etc.) are
reported [Google, 2022] to exhibit malicious behaviours, including privacy
stealing, unwanted programs installed, etc. To counteract these threats, many
works have been proposed that rely on static analysis techniques to detect such
issues. However, static techniques are not sufficient on their own to detect
such defects precisely. This will likely yield false positive results as static
analysis has to make some trade-offs when handling complicated cases (e.g.,
object-sensitive vs. object-insensitive). In addition, static analysis
techniques will also likely suffer from soundness issues because some
complicated features (e.g., reflection, obfuscation, and hardening) are
difficult to be handled [Sun et al., 2021b, Samhi et al., 2022].Comment: Thesi
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