  Website fingerprinting (WF) attacks identify the websites visited over
anonymized connections by analyzing patterns in network traffic flows, such as
packet sizes, directions, or interval times using a machine learning
classifier. Previous studies showed WF attacks achieve high classification
accuracy. However, several issues call into question whether existing WF
approaches are realizable in practice and thus motivate a re-exploration. Due
to Tor's performance issues and resulting poor browsing experience, the vast
majority of users opt for Virtual Private Networking (VPN) despite VPNs weaker
privacy protections. Many other past assumptions are increasingly unrealistic
as web technology advances. Our work addresses several key limitations of prior
art. First, we introduce a new approach that classifies entire websites rather
than individual web pages. Site-level classification uses traffic from all site
components, including advertisements, multimedia, and single-page applications.
Second, our Convolutional Neural Network (CNN) uses only the jitter and size of
500 contiguous packets from any point in a TCP stream, in contrast to prior
work requiring heuristics to find page boundaries. Our seamless approach makes
eavesdropper attack models realistic. Using traces from a controlled browser,
we show our CNN matches observed traffic to a website with over 90% accuracy.
We found the training traffic quality is critical as classification accuracy is
significantly reduced when the training data lacks variability in network
location, performance, and clients' computational capability. We enhanced the
base CNN's efficacy using domain adaptation, allowing it to discount irrelevant
features, such as network location. Lastly, we evaluate several defensive
strategies against seamless WF attacks.
