相关论文: BenchPress: Analyzing Android App Vulnerability Be…
The Android mining sandbox approach consists in running dynamic analysis tools on a benign version of an Android app and recording every call to sensitive APIs. Later, one can use this information to (a) prevent calls to other sensitive…
The Android Open Source Project (AOSP) is probably the most used and customized operating system for smartphones and IoT devices worldwide. Its market share and high adaptability makes Android an interesting operating system for many…
Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for…
Many Android applications collect data from users. When they do, they must protect this collected data according to the current legal frameworks. Such data protection has become even more important since the European Union rolled out the…
The proliferation of mobile applications and the subsequent sharing of personal data with service and application providers have given rise to substantial privacy concerns. Application marketplaces have introduced mechanisms to conform to…
Android's security model severely limits the capabilities of anti-malware software. Unlike commodity anti-malware solutions on desktop systems, their Android counterparts run as sandboxed applications without root privileges and are limited…
While mobile app evolution has been widely studied, geographical variation in app behavior remains largely unexplored. This paper presents a large-scale study of location-based Android app differentiation, uncovering two important and…
In evaluating detection methods, the malware research community relies on scan results obtained from online platforms such as VirusTotal. Nevertheless, given the lack of standards on how to interpret the obtained data to label apps,…
The open-source nature of the Android OS makes it possible for manufacturers to ship custom versions of the OS along with a set of pre-installed apps, often for product differentiation. Some device vendors have recently come under scrutiny…
Repackaging is a serious threat to the Android ecosystem as it deprives app developers of their benefits, contributes to spreading malware on users' devices, and increases the workload of market maintainers. In the space of six years, the…
The Android ecosystem faces a notable challenge known as fragmentation, which denotes the extensive diversity within the system. This issue is mainly related to differences in system versions, device hardware specifications, and…
Developers often build software on top of third-party libraries (Libs) to improve productivity, but these libraries may contain vulnerabilities that enable supply chain attacks. Existing tools detect vulnerable dependencies, yet developers…
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…
Most contemporary mobile devices offer hardware-backed storage for cryptographic keys, user data, and other sensitive credentials. Such hardware protects credentials from extraction by an adversary who has compromised the main operating…
In this work, we present a new benchmarking suite with new real-life inspired skewed workloads to test the performance of concurrent index data structures. We started this project to prepare workloads specifically for self-adjusting data…
With the development of foundation AI technologies, task-executable voice assistants (VAs) have become more popular, enhancing user convenience and expanding device functionality. Android task-executable VAs are applications that are…
Android's permission system is designed to balance usability with informed consent, yet two legacy mechanisms still undermine that balance in Android 16: (i) permission groups that silently auto-grant new permissions within a group after a…
As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety of algorithms and feature sets have been presented to date,…
Many developers and organizations implement apps for Android, the most widely used operating system for mobile devices. Common problems developers face are the various hardware devices, customized Android variants, and frequent updates,…
Android framework-specific app crashes are hard to debug. Indeed, the callback-based event-driven mechanism of Android challenges crash localization techniques that are developed for traditional Java programs. The key challenge stems from…