  Users' website browsing history contains sensitive information, like health
conditions, political interests, financial situations, etc. Some recent studies
have demonstrated the possibility of inferring website fingerprints based on
important usage information such as traffic, cache usage, memory usage, CPU
activity, power consumption, and hardware performance counters information.
However, existing website fingerprinting attacks demand a high sampling rate
which causes high performance overheads and large network traffic, and/or they
require launching an additional malicious website by the user, which is not
guaranteed. As a result, such drawbacks make the existing attacks more
noticeable to users and corresponding fingerprinting detection mechanisms. In
response, in this work, we propose Leaked-Web, a novel accurate and efficient
machine learning-based website fingerprinting attack through processor's
Hardware Performance Counters (HPCs). Leaked-Web efficiently collects hardware
performance counters in users' computer systems at a significantly low
granularity monitoring rate and sends the samples to the remote attack's server
for further classification. Leaked-Web examines the web browsers'
microarchitectural features using various advanced machine learning algorithms
ranging from classical, boosting, deep learning, and time-series models. Our
experimental results indicate that Leaked-Web based on a LogitBoost ML
classifier using only the top 4 HPC features achieves 91% classification
accuracy outperforming the state-of-the-art attacks by nearly 5%. Furthermore,
our proposed attack obtains a negligible performance overhead (only <1%),
around 12% lower than the existing hardware-assisted website fingerprinting
attacks.
