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1. FingerprinTV: Fingerprinting Smart TV Apps

   https://doi.org/10.56553/popets-2022-0088  

   Varmarken, Janus; Al Aaraj, Jad; Trimananda, Rahmadi; Markopoulou, Athina (July 2022, Proceedings on Privacy Enhancing Technologies)

   This paper proposes FingerprinTV, a fully automated methodology for extracting fingerprints from the network traffic of smart TV apps and assessing their performance. FingerprinTV (1) installs, repeatedly launches, and collects network traffic from smart TV apps; (2) extracts three different types of network fingerprints for each app, i.e., domain-based fingerprints (DBF), packet-pair-based fingerprints (PBF), and TLS-based fingerprints (TBF); and (3) analyzes the extracted fingerprints in terms of their prevalence, distinctiveness, and sizes. From applying FingerprinTV to the top-1000 apps of the three most popular smart TV platforms, we find that smart TV app network fingerprinting is feasible and effective: even the least prevalent type of fingerprint manifests itself in at least 68% of apps of each platform, and up to 89% of fingerprints uniquely identify a specific app when two fingerprinting techniques are used together. By analyzing apps that exhibit identical fingerprints, we find that these apps often stem from the same developer or “no code” app generation toolkit. Furthermore, we show that many apps that are present on all three platforms exhibit platformspecific fingerprints. 

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2. The TV is Smart and Full of Trackers: Measuring Smart TV Advertising and Tracking

   https://doi.org/10.2478/popets-2020-0021  

   Varmarken, Janus; Le, Hieu; Shuba, Anastasia; Markopoulou, Athina; Shafiq, Zubair (April 2020, Proceedings on Privacy Enhancing Technologies)

   null (Ed.)

   Abstract In this paper, we present a large-scale measurement study of the smart TV advertising and tracking ecosystem. First, we illuminate the network behavior of smart TVs as used in the wild by analyzing network traffic collected from residential gateways. We find that smart TVs connect to well-known and platform-specific advertising and tracking services (ATSes). Second, we design and implement software tools that systematically explore and collect traffic from the top-1000 apps on two popular smart TV platforms, Roku and Amazon Fire TV. We discover that a subset of apps communicate with a large number of ATSes, and that some ATS organizations only appear on certain platforms, showing a possible segmentation of the smart TV ATS ecosystem across platforms. Third, we evaluate the (in)effectiveness of DNS-based blocklists in preventing smart TVs from accessing ATSes. We highlight that even smart TV-specific blocklists suffer from missed ads and incur functionality breakage. Finally, we examine our Roku and Fire TV datasets for exposure of personally identifiable information (PII) and find that hundreds of apps exfiltrate PII to third parties and platform domains. We also find evidence that some apps send the advertising ID alongside static PII values, effectively eliminating the user’s ability to opt out of ad personalization. 

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3. FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic

   https://doi.org/10.14722/ndss.2020.24412  

   van Ede, Thijs; Bortolameotti, Riccardo; Continella, Andrea; Ren, Jingjing; Dubois, Daniel J.; Lindorfer, Martina; Choffnes, David; van Steen, Maarten; Peter, Andreas (February 2020, Network and Distributed System Security Symposium (NDSS))

   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 state-of-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. 

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4. Inter-app communication between Android apps developed in app-inventor and Android studio

   https://doi.org/10.1145/2897073.2897117  

   Allison, Lance A.; Fuad, Mohammad Muztaba (January 2016, MOBILESoft '16 Proceedings of the International Workshop on Mobile Software Engineering and Systems)

   Communications between mobile apps are an important aspect of mobile platforms. Android is specifically designed with inter-app communication in mind and depends on this to provide different platform specific functionalities. Android Apps can either be designed with the help of Android SDK and using IDEs such as Android Studio or by using a browser based platform called App Inventor. These two development platforms provide their own technique for inter-app communication in the same platform, however lack an established method of inter-app communication when apps are developed using the two seperate development platforms. This paper provides the missing information required for the app communications and presents the method for sending and receiving arguments between apps developed in these two platforms. The paper also outlines the significance of the result, and examines their limitations. 

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5. 50 Ways to Leak Your Data: An Exploration of Apps' Circumvention of the Android Permissions System

   Reardon, Joel; Feal, Alvaro; Wijesekera, Primal; Elazari Bar On, Amit; Vallina-Rodriguez, Narseo; Egelman, Serge (August 2019, 28th USENIX Security Symposium)

   Modern smartphone platforms implement permission-based models to protect access to sensitive data and system resources. However, apps can circumvent the permission model and gain access to protected data without user consent by using both covert and side channels. Side channels present in the implementation of the permission system allow apps to access protected data and system resources without permission; whereas covert channels enable communication between two colluding apps so that one app can share its permission-protected data with another app lacking those permissions. Both pose threats to user privacy. In this work, we make use of our infrastructure that runs hundreds of thousands of apps in an instrumented environment. This testing environment includes mechanisms to monitor apps' runtime behaviour and network traffic. We look for evidence of side and covert channels being used in practice by searching for sensitive data being sent over the network for which the sending app did not have permissions to access it. We then reverse engineer the apps and third-party libraries responsible for this behaviour to determine how the unauthorized access occurred. We also use software fingerprinting methods to measure the static prevalence of the technique that we discover among other apps in our corpus. Using this testing environment and method, we uncovered a number of side and covert channels in active use by hundreds of popular apps and third-party SDKs to obtain unauthorized access to both unique identifiers as well as geolocation data. We have responsibly disclosed our findings to Google and have received a bug bounty for our work. 

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