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1. FingerprinTV: Fingerprinting Smart TV Apps

   J. Varmarken, J. Al (July 2022, Proceedings of the Privacy Enhancing Technologies Symposium (PETS) 2022)

   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 platform-specific 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. 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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5. Your Phone is My Proxy: Detecting and Understanding Mobile Proxy Networks

   https://doi.org/10.14722/ndss.2021.24008  

   Mi, Xianghang; Tang, Siyuan; Li, Zhengyi; Liao, Xiaojing; Qian, Feng; Wang, XiaoFeng (January 2021, Proceeding of ISOC Network and Distributed System Security Symposium (NDSS), 2021)

   null (Ed.)

   Residential proxy has emerged as a service gaining popularity recently, in which proxy providers relay their customers’ network traffic through millions of proxy peers under their control. We find that many of these proxy peers are mobile devices, whose role in the proxy network can have significant security implications since mobile devices tend to be privacy and resource-sensitive. However, little effort has been made so far to understand the extent of their involvement, not to mention how these devices are recruited by the proxy network and what security and privacy risks they may pose. In this paper, we report the first measurement study on the mobile proxy ecosystem. Our study was made possible by a novel measurement infrastructure, which enabled us to identify proxy providers, to discover proxy SDKs (software development kits), to detect Android proxy apps built upon the proxy SDKs, to harvest proxy IP addresses, and to understand proxy traffic. The information collected through this infrastructure has brought to us new understandings of this ecosystem and important security discoveries. More specifically, 4 proxy providers were found to offer app developers mobile proxy SDKs as a competitive app monetization channel, with $50K per month per 1M MAU (monthly active users). 1,701 Android APKs (belonging to 963 Android apps) turn out to have integrated those proxy SDKs, with most of them available on Google Play with at least 300M installations in total. Furthermore, 48.43% of these APKs are flagged by at least 5 anti-virus engines as malicious, which could explain why 86.60% of the 963 Android apps have been removed from Google Play by Oct 2019. Besides, while these apps display user consent dialogs on traffic relay, our user study indicates that the user consent texts are quite confusing. We even discover a proxy SDK that stealthily relays traffic without showing any notifications. We also captured 625K cellular proxy IPs, along with a set of suspicious activities observed in proxy traffic such as ads fraud. We have reported our findings to affected parties, offered suggestions, and proposed the methodologies to detect proxy apps and proxy traffic. 

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