Biblio

Browser fingerprinting is a widely used technique to uniquely identify web users and to track their online behavior. Until now, different tools have been proposed to protect the user against browser fingerprinting. However, these tools have usability restrictions as they deactivate browser features and plug-ins (like Flash) or the HTML5 canvas element. In addition, all of them only provide limited protection, as they randomize browser settings with unrealistic parameters or have methodical flaws, making them detectable for trackers. In this work we demonstrate the first anti-fingerprinting strategy, which protects against Flash fingerprinting without deactivating it, provides robust and undetectable anti-canvas fingerprinting, and uses a large set of real word data to hide the actual system and browser properties without losing usability. We discuss the methods and weaknesses of existing anti-fingerprinting tools in detail and compare them to our enhanced strategies. Our evaluation against real world fingerprinting tools shows a successful fingerprinting protection in over 99% of 70.000 browser sessions.

Security and privacy are crucial for all IT systems and services. The diversity of applications places high demands on the knowledge and experience of software developers and IT professionals. Besides programming skills, security and privacy aspects are required as well and must be considered during development. If developers have not been trained in these topics, it is especially difficult for them to prevent problematic security issues such as vulnerabilities. In this work we present an interactive e-learning platform focusing on solving real-world cybersecurity tasks in a sandboxed web environment. With our platform students can learn and understand how security vulnerabilities can be exploited in different scenarios. The platform has been evaluated in four university IT security courses with around 1100 participants over three years.