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Users' website browsing history contains sensitive information, like healthconditions, political interests, financial situations, etc. Some recent studieshave demonstrated the possibility of inferring website fingerprints based onimportant usage information such as traffic, cache usage, memory usage, CPUactivity, power consumption, and hardware performance counters information.However, existing website fingerprinting attacks demand a high sampling ratewhich causes high performance overheads and large network traffic, and/or theyrequire launching an additional malicious website by the user, which is notguaranteed. As a result, such drawbacks make the existing attacks morenoticeable to users and corresponding fingerprinting detection mechanisms. Inresponse, in this work, we propose Leaked-Web, a novel accurate and efficientmachine learning-based website fingerprinting attack through processor'sHardware Performance Counters (HPCs). Leaked-Web efficiently collects hardwareperformance counters in users' computer systems at a significantly lowgranularity monitoring rate and sends the samples to the remote attack's serverfor further classification. Leaked-Web examines the web browsers'microarchitectural features using various advanced machine learning algorithmsranging from classical, boosting, deep learning, and time-series models. Ourexperimental results indicate that Leaked-Web based on a LogitBoost MLclassifier using only the top 4 HPC features achieves 91% classificationaccuracy 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 fingerprintingattacks.

Leaked-Web: Accurate and Efficient Machine Learning-Based Website Fingerprinting Attack through Hardware Performance Counters