Adblocking relies on filter lists, which are manually curated and maintained by a community of filter list authors. Filter list curation is a laborious process that does not scale well to a large number of sites or over time. In this article, we introduce AutoFR, a reinforcement learning framework to fully automate the process of filter rule creation and evaluation for sites of interest. We design an algorithm based on multi-arm bandits to generate filter rules that block ads while controlling the trade-off between blocking ads and avoiding visual breakage. We test AutoFR on thousands of sites and show that it is efficient: It takes only a few minutes to generate filter rules for a site of interest. AutoFR is effective: It optimizes filter rules for a particular site that can block 86% of the ads, as compared to 87% by EasyList, while achieving comparable visual breakage. Using AutoFR as a building block, we devise three methodologies that generate filter rules across sites based on: (1) a modified version of AutoFR, (2) rule popularity, and (3) site similarity. We conduct an in-depth comparative analysis of these approaches by considering their effectiveness, efficiency, and maintainability. We demonstrate that some of them can generalize well to new sites in both controlled and live settings. We envision that AutoFR can assist the adblocking community in automatically generating and updating filter rules at scale.

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.