Abstract:
Hidden services (HS) allow users to experience anonymity, but they also provide shelter for criminal activities. The widespread attention towards deanonymizing HS has bro...Show MoreMetadata
Abstract:
Hidden services (HS) allow users to experience anonymity, but they also provide shelter for criminal activities. The widespread attention towards deanonymizing HS has brought website fingerprinting attack (WFA) into the spotlight, which is considered highly promising. However, most HS websites are designed simply and have high similarity in resource structures, making it difficult to represent the HS access traffic well, and existing work often directly applies traffic representation methods in the field of web research, resulting in poor effects of the model. Besides, features of HS access traffic are closely related to the resource access sequence of websites. Current studies build models based on convolutional neural network (CNN), ignoring the global correlation of HS access traffic parts. To address the short-comings, we have proposed an efficient WFA to deanonymize HS, and named it NuanceTracker. The contribution of our work lies in three points. Firstly, a burst-based HS fingerprint generation algorithm is proposed to describe the sequence of HS access traffic. Secondly, we propose NuanceTracker, which is designed by introducing multi-scale global attention (MGA) into a basic CNN model for global information extraction. Finally, comparison experiments are conducted in closed-world and open-world scenarios. Our NuanceTracker has proven to outperform three state-of-the-art WFA methods.
Date of Conference: 26-29 June 2024
Date Added to IEEE Xplore: 31 October 2024
ISBN Information: