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Case Study: Disclosure of Indirect Device Fingerprinting in Privacy Policies
Recent developments in online tracking make it harder for individuals to...
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Do Cookie Banners Respect my Choice? Measuring Legal Compliance of Banners from IAB Europe's Transparency and Consent Framework
As a result of the GDPR and the ePrivacy Directive, European users encou...
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Deep Reinforcement Learning for Detecting Malicious Websites
Phishing is the simplest form of cybercrime with the objective of baitin...
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Detecting a network of hijacked journals by its archive
This study describes a method to detect hijacked journals based on the a...
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Traffic Analysis with Deep Learning
Deep Neural Networks (DNN) has obtained enormous attention with its adva...
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Demystifying content-blockers: A large scale study of actual performance gains
With the evolution of the online advertisement and tracking ecosystem, c...
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Tracing Cryptocurrency Scams: Clustering Replicated Advance-Fee and Phishing Websites
Over the past few years, there has been a growth in activity, public kno...
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Fingerprinting the Fingerprinters: Learning to Detect Browser Fingerprinting Behaviors
Browser fingerprinting is an invasive and opaque stateless tracking technique. Browser vendors, academics, and standards bodies have long struggled to provide meaningful protections against browser fingerprinting that are both accurate and do not degrade user experience. We propose FP-Inspector, a machine learning based syntactic-semantic approach to accurately detect browser fingerprinting. We show that FP-Inspector performs well, allowing us to detect 26 API-level fingerprinting countermeasure, built upon FP-Inspector, helps reduce website breakage by a factor of 2. We use FP-Inspector to perform a measurement study of browser fingerprinting on top-100K websites. We find that browser fingerprinting is now present on more than 10 over a quarter of the top-10K websites. We also discover previously unreported uses of JavaScript APIs by fingerprinting scripts suggesting that they are looking to exploit APIs in new and unexpected ways.
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