Mobile-application fingerprinting of network traffic is valuable for many security solutions as it provides insights into the apps active on a network. Unfortunately, existing techniques require prior knowledge of apps to be able to recognize them. However, mobile environments are constantly evolving, i.e., apps are regularly installed, updated, and uninstalled. Therefore, it is infeasible for existing fingerprinting approaches to cover all apps that may appear on a network. Moreover, most mobile traffic is encrypted, shows similarities with other apps, e.g., due to common libraries or the use of content delivery networks, and depends on user input, further complicating the fingerprinting process. As a solution, we propose FLOWPRINT, a semi-supervised approach for fingerprinting mobile apps from (encrypted) network traffic. We automatically find temporal correlations among destination-related features of network traffic and use these correlations to generate app fingerprints. Our approach is able to fingerprint previously unseen apps, something that existing techniques fail to achieve. We evaluate our approach for both Android and iOS in the setting of app recognition, where we achieve an accuracy of 89.2%, significantly outperforming stateof-the-art solutions. In addition, we show that our approach can detect previously unseen apps with a precision of 93.5%, detecting 72.3% of apps within the first five minutes of communication.

Abstract-Is mobile privacy getting better or worse over time? In this paper, we address this question by studying privacy leaks from historical and current versions of 512 popular Android apps, covering 7,665 app releases over 8 years of app version history. Through automated and scripted interaction with apps and analysis of the network traffic they generate on real mobile devices, we identify how privacy changes over time for individual apps and in aggregate. We find several trends that include increased collection of personally identifiable information (PII) across app versions, slow adoption of HTTPS to secure the information sent to other parties, and a large number of third parties being able to link user activity and locations across apps. Interestingly, while privacy is getting worse in aggregate, we find that the privacy risk of individual apps varies greatly over time, and a substantial fraction of apps see little change or even improvement in privacy. Given these trends, we propose metrics for quantifying privacy risk and for providing this risk assessment proactively to help users balance the risks and benefits of installing new versions of apps.

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In this paper we outline the idea to adopt gamification techniques to engage, train, monitor, and motivate all the players involved in the development of complex software artifacts, from the inception to the deployment and maintenance. The paper introduces the concept of gamification and proposes a research approach to understand how its principles may be successfully applied to the process of software development. Applying gamification to software engineering is not as straightforward as it may appear since it has to be casted to the peculiarities of this domain. Existing literature in the area has already recognized the possible use of such technology in the context of software development, however how to design and use gamification in this context is still an open question. This leads to several research challenges which are organized in a fascinating research agenda that is part of the contribution of this paper. Finally, to support the proposed ideas we present a preliminary experiment that shows the effect of gamification on the performance of students involved in a software engineering project.

Internet of Things (IoT) devices are increasingly found in everyday homes, providing useful functionality for devices such as TVs, smart speakers, and video doorbells. Along with their benefits come potential privacy risks, since these devices can communicate information about their users to other parties over the Internet. However, understanding these risks in depth and at scale is difficult due to heterogeneity in devices' user interfaces, protocols, and functionality.In this work, we conduct a multidimensional analysis of information exposure from 81 devices located in labs in the US and UK. Through a total of 34,586 rigorous automated and manual controlled experiments, we characterize information exposure in terms of destinations of Internet traffic, whether the contents of communication are protected by encryption, what are the IoT-device interactions that can be inferred from such content, and whether there are unexpected exposures of private and/or sensitive information (e.g., video surreptitiously transmitted by a recording device). We highlight regional differences between these results, potentially due to different privacy regulations in the US and UK. Last, we compare our controlled experiments with data gathered from an in situ user study comprising 36 participants.