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.

Bridge the Gap is a trend that aims to allow web browsers to start smartphone apps on a mobile device. This is achieved by so-called Deep Links, which enable direct linking to specific in-app resources. However, the resulting fusion of the web and native apps also introduces new attack vectors. There are numerous studies on security and privacy concerns of Deep Links on the open-source operating system Android, showing that these are prone to threats such as hijacking. The proprietary operating system iOS has a similar implementation of deep linking mechanisms to Android. However, there are not many publications on this matter, possibly due to the unavailability of iOS’ source code. In this thesis, we investigate the security of mobile Deep Links. First, we present known attack scenarios for Android with regards to Custom Schemes and App Links. Then, we consider the applicability of these attack vectors to deep linking mechanisms on iOS. Therefore, we develop vulnerable apps implementing discussed security issues, analyze whether an attacker could abuse them, and what security and privacy implications this has. Next, we compare our results to the corresponding mechanisms and security concerns of Android. Finally, to gain an insight into the actual security implications of the presented attack vectors, we analyze the distribution of Deep Links in the wild, based on a dataset containing over 11,000 iOS apps from the official Apple App Store.