XAI-Website-Fingerprinting

Website Fingerprinting attacks aim to track the visited websites in browsers and infer confidential information about users. The presence of large numbers of websites on internet has made it necessary to integrate more sophisticated and automated techniques to analyze the collected data. Thanks to recent advancements in Machine Learning and Artificial Intelligence algorithms, website fingerprinting has been implemented efficiently in a large-scale. Nevertheless, trained models for website detection are mostly treated as a black-box where the source of information leakage is not visible to both attackers and security researchers. This study provides a dataset of side-channel measurements and their corresponding network traces to enable other researchers to analyze the data further.