DeepCoAST Dataset

Citation Author(s):
Goun
Kim
Ewha Womans University
HyeonJeong
Kwak
Ewha Womans University
Sujin
Kim
Ewha Womans University
Se Eun
Oh
Ewha Womans University
Submitted by:
Goun Kim
Last updated:
Tue, 07/16/2024 - 16:53
DOI:
10.21227/9chd-ng79
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Abstract 

Our DeepCoAST dataset specifically explores the vulnerabilities of various traffic-splitting Website Fingerprinting (WF) Defenses, such as TrafficSliver, HyWF, and CoMPS. Our dataset comprises defended traces generated from the BigEnough dataset, which includes Tor cell trace instances of 95 websites, each represented by 200 instances collected under the standard browser security level. We simulated the traffic-splitting defenses assuming there are two split traces from the vanilla trace. Additionally, we set four configurations for TrafficSliver, adjusting the number of Tor cells and path selection probabilities to simulate realistic network conditions. We set one configuration for HyWF and CoMPS, and all configurations follow the parameters from the papers. We created the train and test dataset by extracting the various features such as Direction, Tik-Tok, ICD, ICDS, and 1-D TAM mentioned in our paper. Our dataset offers a comprehensive basis for evaluating traffic-splitting WF defenses.

Funding Agency: 
Korea government(MSIT), Ewha Womans University
Grant Number: 
RS-2023-00222385, RS-2022-00166669, RS-2022-00155966, Ewha Womans University Research Grant of 2022