Abstract For years, researchers have been analyzing mobile Android apps to investigate diverse properties such as software engineering practices, business models, security, privacy, or usability, as well as differences between marketplaces. While similar studies on iOS have been limited, recent work has started to analyze and compare Android apps with those for iOS. To obtain the most representative analysis results across platforms, the ideal approach is to compare their characteristics and behavior for the same set of apps, e. g., to study a set of apps for iOS and their respective counterparts for Android. Previous work has only attempted to identify and evaluate such cross-platform apps to a limited degree, mostly comparing sets of apps independently drawn from app stores, manually matching small sets of apps, or relying on brittle matches based on app and developer names. This results in (1) comparing apps whose behavior and properties significantly differ, (2) limited scalability, and (3) the risk of matching only a small fraction of apps. In this work, we propose a novel approach to create an extensive dataset of cross-platform apps for the iOS and Android ecosystems. We describe an analysis pipeline for discovering, retrieving, and matching apps from the Apple App Store and Google Play Store that we used to create a set of 3,322 cross-platform apps out of 10,000 popular apps for iOS and Android, respectively. We evaluate existing and new approaches for cross-platform app matching against a set of reference pairs that we obtained from Google's data migration service. We identify a combination of seven features from app store metadata and the apps themselves to match iOS and Android apps with high confidence (95.82 %). Compared to previous attempts that identified 14 % of apps as cross-platform, we are able to match 34 % of apps in our dataset. To foster future research in the cross-platform analysis of mobile apps, we make our pipeline available to the community.

Abstract: Virtual Machine Introspection (VMI) is a powerful technology used to detect and analyze malicious software inside Virtual Machines (VMs) from outside. Asynchronously accessing the VM ’s memory can be insufficient for efficiently monitoring what is happening inside of a VM. Active VMI introduces breakpoints to intercept VM execution at relevant points. Especially for frequently visited breakpoints, and even more so for production systems, it is crucial to keep their performance overhead as low as possible. In this paper, we provide a systematization of existing VMI breakpoint implementation variants, propose workloads to quantify the different performance penalties of breakpoints, and implement them in the benchmarking application bpbench. We used this benchmark to measure that, on an Intel Core i5 7300U, SmartVMI’s breakpoints take around 81 μs to handle, and keeping the breakpoint invisible costs an additional 21 μs per read access. The availability of bpbench facilitates the comparison of disparate breakpoint mechanisms and their performance optimization with immediate feedback.

This paper discusses the transformative potential of 6G technology and the tactile Internet in reshaping participatory healthcare models while architecturing these digital healthcare systems with security and resiliency by design. As healthcare continues to advance towards more inclusive and patient-centered approaches, the role of emerging technologies like mobile health, 6G, and the Internet will become increasingly significant in facilitating these interactions while ensuring the security and privacy of patient data. Furthermore, the organizations providing healthcare to patients must ensure compliance with different regulations, which are also focusing more and more on cybersecurity issues.