I Spy with My Little Eye: Analysis and Detection of Spying Browser Extensions
Anupama Aggarwal, Bimal Viswanath, Saravana Kumar, Ayush Shah, Liang, Zhang, Ponnurangam Kumaraguru

TL;DR
This paper investigates spying browser extensions that can capture complete user activity and communicate sensitive data externally, analyzing their behavior and proposing an RNN-based detection method with high accuracy.
Contribution
It provides an empirical analysis of spying extensions' behaviors and introduces a machine learning approach using RNNs for effective detection.
Findings
Spying extensions steal browsing history, social tokens, IP, and geolocation.
RNN-based detection achieves over 90% precision and recall.
Sequence of API calls is a robust feature for identifying spying extensions.
Abstract
Several studies have been conducted on understanding third-party user tracking on the web. However, web trackers can only track users on sites where they are embedded by the publisher, thus obtaining a fragmented view of a user's online footprint. In this work, we investigate a different form of user tracking, where browser extensions are repurposed to capture the complete online activities of a user and communicate the collected sensitive information to a third-party domain. We conduct an empirical study of spying browser extensions on the Chrome Web Store. First, we present an in-depth analysis of the spying behavior of these extensions. We observe that these extensions steal a variety of sensitive user information, such as the complete browsing history (e.g., the sequence of web traversals), online social network (OSN) access tokens, IP address, and user geolocation. Second, we…
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