ﻻ يوجد ملخص باللغة العربية
While there have been various studies towards Android apps and their development, there is limited discussion of the broader class of apps that fall in the fake area. Fake apps and their development are distinct from official apps and belong to the mobile underground industry. Due to the lack of knowledge of the mobile underground industry, fake apps, their ecosystem and nature still remain in mystery. To fill the blank, we conduct the first systematic and comprehensive empirical study on a large-scale set of fake apps. Over 150,000 samples related to the top 50 popular apps are collected for extensive measurement. In this paper, we present discoveries from three different perspectives, namely fake sample characteristics, quantitative study on fake samples and fake authors developing trend. Moreover, valuable domain knowledge, like fake apps naming tendency and fake developers evasive strategies, is then presented and confirmed with case studies, demonstrating a clear vision of fake apps and their ecosystem.
Third-party security apps are an integral part of the Android app ecosystem. Many users install them as an extra layer of protection for their devices. There are hundreds of such security apps, both free and paid in Google Play Store and some of them
Mobile health applications (mHealth apps for short) are being increasingly adopted in the healthcare sector, enabling stakeholders such as governments, health units, medics, and patients, to utilize health services in a pervasive manner. Despite havi
We study the temporal dynamics of potentially harmful apps (PHAs) on Android by leveraging 8.8M daily on-device detections collected among 11.7M customers of a popular mobile security product between 2019 and 2020. We show that the current security m
Modern browsers give access to several attributes that can be collected to form a browser fingerprint. Although browser fingerprints have primarily been studied as a web tracking tool, they can contribute to improve the current state of web security
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-lev