English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Website fingerprinting attacks, which use statistical analysis on networktraffic to compromise user privacy, have been shown to be effective even if thetraffic is sent over anonymity-preserving networks such as Tor. The classicalattack model used to evaluate website fingerprinting attacks assumes an on-pathadversary, who can observe all traffic traveling between the user's computerand the Tor network. In this work we investigate these attacks under adifferent attack model, in which the adversary is capable of running a smallamount of unprivileged code on the target user's computer. Under this model,the attacker can mount cache side-channel attacks, which exploit the effects ofcontention on the CPU's cache, to identify the website being browsed. In animportant special case of this attack model, a JavaScript attack is launchedwhen the target user visits a website controlled by the attacker. Theeffectiveness of this attack scenario has never been systematically analyzed,especially in the open-world model which assumes that the user is visiting amix of both sensitive and non-sensitive sites. In this work we show that cachewebsite fingerprinting attacks in JavaScript are highly feasible, even whenthey are run from highly restrictive environments, such as the Tor Browser.Specifically, we use machine learning techniques to classify traces of cacheactivity. Unlike prior works, which try to identify cache conflicts, our workmeasures the overall occupancy of the last-level cache. We show that ourapproach achieves high classification accuracy in both the open-world and theclosed-world models. We further show that our techniques are resilient both tonetwork-based defenses and to side-channel countermeasures introduced to modernbrowsers as a response to the Spectre attack.

Robust Website Fingerprinting Through the Cache Occupancy Channel