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We conduct the first systematic study of the effectiveness of Web Audio API-based browser fingerprinting mechanisms and present new insights. First, we show that audio fingerprinting vectors, unlike other prior vectors, reveal an apparent fickleness with some users' browsers giving away differing fingerprints in repeated attempts. However, we show that it is possible to devise a graph-based analysis mechanism to collectively consider all the different fingerprints left by users' browsers and thus craft a highly stable fingerprinting mechanism. Next, we investigate the diversity of audio fingerprints and compare this with prior fingerprinting techniques. Our results show that audio fingerprints are much less diverse than other vectors with only 95 distinct fingerprints among 2093 users. At the same time, further analysis shows that web audio fingerprinting can potentially bring considerable additive value to existing fingerprinting mechanisms. For instance, our results show that the addition of web audio fingerprinting causes a 9.6\% increase in entropy when compared to using Canvas fingerprinting alone. We also show that our results contradict the current security and privacy recommendations provided by W3C regarding audio fingerprinting. 

The rapid growth of online advertising has fueled the growth of ad-blocking software, such as new ad-blocking and privacy-oriented browsers or browser extensions. In response, both ad publishers and ad networks are constantly trying to pursue new strategies to keep up their revenues. To this end, ad networks have started to leverage the Web Push technology enabled by modern web browsers. As web push notifications (WPNs) are relatively new, their role in ad delivery has not yet been studied in depth. Furthermore, it is unclear to what extent WPN ads are being abused for malvertising (i.e., to deliver malicious ads). In this paper, we aim to fill this gap. Specifically, we propose a system called PushAdMiner that is dedicated to (1) automatically registering for and collecting a large number of web-based push notifications from publisher websites, (2) finding WPN-based ads among these notifications, and (3) discovering malicious WPN-based ad campaigns. Using PushAdMiner, we collected and analyzed 21,541 WPN messages by visiting thousands of different websites. Among these, our system identified 572 WPN ad campaigns, for a total of 5,143 WPN-based ads that were pushed by a variety of ad networks. Furthermore, we found that 51% of all WPN ads we collected are malicious, and that traditional ad-blockers and URL filters were mostly unable to block them, thus leaving a significant abuse vector unchecked.