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Several studies have been conducted on understanding third-party usertracking on the web. However, web trackers can only track users on sites wherethey are embedded by the publisher, thus obtaining a fragmented view of auser's online footprint. In this work, we investigate a different form of usertracking, where browser extensions are repurposed to capture the completeonline activities of a user and communicate the collected sensitive informationto a third-party domain. We conduct an empirical study of spying browserextensions on the Chrome Web Store. First, we present an in-depth analysis ofthe spying behavior of these extensions. We observe that these extensions steala variety of sensitive user information, such as the complete browsing history(e.g., the sequence of web traversals), online social network (OSN) accesstokens, IP address, and user geolocation. Second, we investigate the potentialfor automatically detecting spying extensions by applying machine learningschemes. We show that using a Recurrent Neural Network (RNN), the sequences ofbrowser API calls can be a robust feature, outperforming hand-crafted features(used in prior work on malicious extensions) to detect spying extensions. OurRNN based detection scheme achieves a high precision (90.02%) and recall(93.31%) in detecting spying extensions.

I Spy with My Little Eye: Analysis and Detection of Spying Browser Extensions