Towards Real-World Design and Evaluation of Website Fingerprinting Defenses

PhD Thesis Proposal Defence


Title: "Towards Real-World Design and Evaluation of Website Fingerprinting 
Defenses"

by

Mr. Jiajun GONG


Abstract:

Website Fingerprinting (WF) attacks threaten user privacy on anonymity networks 
such as Tor because they can be used by network surveillants to identify the 
web pages a user is visiting by extracting the size and timing information of 
the user’s encrypted network traffic; however, Tor is currently undefended 
against WF because existing defenses have not convincingly shown their 
effectiveness. Some defenses have been overcome by newer attacks; other 
defenses have never been implemented and tested in a real open-world scenario, 
as they had unsolved practical issues for deployment. In this thesis, we 
focused on designing and evaluating effective defenses that can be deployed in 
the real Tor network. We proposed three effective defenses that incur different 
overhead levels, targeting users with different security preferences.

We first proposed two zero-delay defenses, FRONT and GLUE. FRONT and GLUE are 
two practical defenses specifically designed for achieving low overhead. We 
observed that WF attacks rely on the feature-rich trace front, so FRONT focuses 
on obfuscating the trace front with dummy packets. It also randomizes the 
number and distribution of dummy packets for trace-to-trace randomness to 
impede the attacker’s learning process. GLUE adds dummy packets between 
separate traces so that they appear to the attacker as a long consecutive 
trace, rendering the attacker unable to find their start or end points, let 
alone classify them. Our experiments show that with only 33% data overhead, 
FRONT reduces the F1-score of the best attack from 0.94 to 0.47. By comparison, 
the best-known lightweight defense, WTF-PAD, only reduces it to 0.70. With 
around 22% — 44% data overhead, GLUE can lower the true positive rate and 
precision of the best WF attacks to less than 15%, approaching the performance 
of the best heavyweight defenses.

FRONT is strong and efficient as a lightweight defense, but it is ineffective 
if we want to reduce the attacker’s true positive rate below 50%. To further 
thwart WF attacks, we proposed a strong defense, Surakav. Surakav makes use of 
a Generative Adversarial Network (GAN) to generate realistic sending patterns 
and regulates buffered data according to these patterns. Experiments show that 
Surakav is able to reduce the attacker’s true positive rate by 57% with 55% 
data overhead and 16% time overhead, saving 42% data overhead compared to 
FRONT. In the heavyweight setting, Surakav outperforms the strongest known 
defense, Tamaraw, requiring 50% less overhead in data and time to lower the 
attacker’s true positive rate to only 8%.


Date:			Wednesday, 5 October 2022

Time:                  	3:00pm - 5:00pm

Venue:                  Room 5501
                         lifts 25/26

Zoom Meeting: 
https://hkust.zoom.us/j/9514345771?pwd=WkEzbTFZVW91dGNMSERpT09peFgvdz09

Committee Members:	Dr. Charles Zhang (Supervisor)
 			Dr. Tao Wang (Supervisor, Simon Fraser University)
 			Prof. Shing-Chi Cheung (Chairperson)
 			Dr. Dimitris Papadopoulos
 			Prof. Raymond Wong


**** ALL are Welcome ****