To remove or not remove Mobile Apps? A data-driven predictive model approach

Mobile app stores are the key distributors of mobile applications. They regularly apply vetting processes to the deployed apps. Yet, some of these vetting processes might be inadequate or applied late. The late removal of applications might have unpleasant consequences for developers and users alike. Thus, in this work we propose a data-driven predictive approach that determines whether the respective app will be removed or accepted. It also indicates the features' relevance that help the stakeholders in the interpretation. In turn, our approach can support developers in improving their apps and users in downloading the ones that are less likely to be removed. We focus on the Google App store and we compile a new data set of 870,515 applications, 56% of which have actually been removed from the market. Our proposed approach is a bootstrap aggregating of multiple XGBoost machine learning classifiers. We propose two models: user-centered using 47 features, and developer-centered using 37 features, the ones only available before deployment. We achieve the following Areas Under the ROC Curves (AUCs) on the test set: user-centered = 0.792, developer-centered = 0.762

Mobile Application Security Platforms Survey

Nowadays Smartphone and other mobile devices have become incredibly important in every aspect of our life. Because they have practically offered same capabilities as desktop workstations as well as come to be powerful in terms of CPU (Central processing Unit), Storage and installing numerous applications. Therefore, Security is considered as an important factor in wireless communication technologies, particularly in a wireless ad-hoc network and mobile operating systems. Moreover, based on increasing the range of mobile application within variety of platforms, security is regarded as on the most valuable and considerable debate in terms of issues, trustees, reliabilities and accuracy. This paper aims to introduce a consolidated report of thriving security on mobile application platforms and providing knowledge of vital threats to the users and enterprises. Furthermore, in this paper, various techniques as well as methods for security measurements, analysis and prioritization within the peak of mobile platforms will be presented. Additionally, increases understanding and awareness of security on mobile application platforms to avoid detection, forensics and countermeasures used by the operating systems. Finally, this study also discusses security extensions for popular mobile platforms and analysis for a survey within a recent research in the area of mobile platform security

Open challenges in vetting the internet‐of‐things

Internet‐of‐Thing (IoT) is a rapid‐emerging technology that exploits the concept of internetwork to connect things such as physical devices and objects together. A huge number of things (6.4 billion are in use in 2016) are already acting without direct human control raising a lot of concerns about the readiness and appropriateness of existing security practices, techniques, and tools to secure the data collected and protect people\u27s private lives. As a first step, this paper presses the importance of having a dedicated process for vetting IoT (by analogy to vetting mobile apps) with focus on exposing things\u27 vulnerabilities that could be the primary source of attacks. These vulnerabilities are identified according to things\u27 duties decomposed into sensing, actuating, and communicating. A set of questions shed light on things\u27 vulnerabilities per type of duty